[StreamInsert](../api/insert.md#streaminsert-rpc)
[MultiInsert](../api/insert.md#multiinsert-rpc) | [Vald Insert APIs](../api/insert.md) | -| Update Service | Update the exists vector(s) in the Vald Agent Pods. | [Update](../api/update.md#update-rpc)
[StreamUpdate](../api/update.md#streamupdate-rpc)
[MultiUpdate](../api/update.md#multiupdate-rpc) | [Vald Update APIs](../api/update.md) | -| Upsert Service | Update the exists vector(s) in the Vald Agent Pods or Insert new vector(s) into the Vald Agent Pods if the vector is not exists. | [Upsert](../api/upsert.md#upsert-rpc)
[StreamUpsert](../api/upsert.md#streamupsert-rpc)
[MultiUpdate](../api/upsert.md#multiupsert-rpc) | [Vald Upsert APIs](../api/upsert.md) | -| Search Service | Search nearest neighbor vectors using query (vectors or IDs).
There are 2 types of Search methods: Search (means ANN Search) and LinearSearch. | [Search](../api/search.md#search-rpc)
[SearchByID](../api/search.md#searchbyid-rpc)
[StreamSearch](../api/search.md#streamsearch-rpc)
[StreamSearchByID](../api/search.md#streamsearchbyid-rpc)
[MultiSearch](../api/search.md#multisearch-rpc)
[MultiSearchByID](../api/search.md#multisearchbyid-rpc)
[LinearSearch](../api/search.md#linearsearch-rpc)
[LinearSearchByID](../api/search.md#linearsearchbyid-rpc)
[StreamLinearSearch](../api/search.md#streamlinearsearch-rpc)
[StreamLinearSearchByID](../api/search.md#streamlinearsearchbyid-rpc)
[MultiLinearSearch](../api/search.md#multilinearsearch-rpc)
[MultiLinearSearchByID](../api/search.md#multilinearsearchbyid-rpc) | [Vald Search APIs](../api/search.md) | -| Remove Service | Remove the exists vector(s) from the Vald Agent Pods. | [Remove](../api/remove.md#remove-rpc)
[StreamRemove](../api/remove.md#streamremove-rpc)
[MultiRemove](../api/remove.md#multiremove-rpc) | [Vald Remove APIs](../api/remove.md) | -| Object Service | Get information of vector(s) indexed into the Vald Agent Pods. | [Exists](../api/object.md#exists-rpc)
[GetObject](../api/object.md#getobject-rpc)
[StreamGetObject](../api/object.md#streamgetobject-rpc) | [Vald Object APIs](../api/object.md) | +| Service | Description | API NAMES | LINK | +| :------------: | :------------------------------------------------------------------------------------------------------------------------------------------------ | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :----------------------------------- | +| Insert Service | Insert new vector(s) into the Vald Agent Pods. | [Insert](../api/insert.md#insert-rpc)
[StreamInsert](../api/insert.md#streaminsert-rpc)
[MultiInsert](../api/insert.md#multiinsert-rpc) | [Vald Insert APIs](../api/insert.md) | +| Update Service | Update the exists vector(s) in the Vald Agent Pods. | [Update](../api/update.md#update-rpc)
[StreamUpdate](../api/update.md#streamupdate-rpc)
[MultiUpdate](../api/update.md#multiupdate-rpc) | [Vald Update APIs](../api/update.md) | +| Upsert Service | Update the exists vector(s) in the Vald Agent Pods or Insert new vector(s) into the Vald Agent Pods if the vector is not exists. | [Upsert](../api/upsert.md#upsert-rpc)
[StreamUpsert](../api/upsert.md#streamupsert-rpc)
[MultiUpdate](../api/upsert.md#multiupsert-rpc) | [Vald Upsert APIs](../api/upsert.md) | +| Search Service | Search nearest neighbor vectors using query (vectors or IDs).
There are 2 types of Search methods: Search (means ANN Search) and LinearSearch. | [Search](../api/search.md#search-rpc)
[SearchByID](../api/search.md#searchbyid-rpc)
[StreamSearch](../api/search.md#streamsearch-rpc)
[StreamSearchByID](../api/search.md#streamsearchbyid-rpc)
[MultiSearch](../api/search.md#multisearch-rpc)
[MultiSearchByID](../api/search.md#multisearchbyid-rpc)
[LinearSearch](../api/search.md#linearsearch-rpc)
[LinearSearchByID](../api/search.md#linearsearchbyid-rpc)
[StreamLinearSearch](../api/search.md#streamlinearsearch-rpc)
[StreamLinearSearchByID](../api/search.md#streamlinearsearchbyid-rpc)
[MultiLinearSearch](../api/search.md#multilinearsearch-rpc)
[MultiLinearSearchByID](../api/search.md#multilinearsearchbyid-rpc) | [Vald Search APIs](../api/search.md) | +| Remove Service | Remove the exists vector(s) from the Vald Agent Pods. | [Remove](../api/remove.md#remove-rpc)
[StreamRemove](../api/remove.md#streamremove-rpc)
[MultiRemove](../api/remove.md#multiremove-rpc) | [Vald Remove APIs](../api/remove.md) | +| Object Service | Get information of vector(s) indexed into the Vald Agent Pods. | [Exists](../api/object.md#exists-rpc)
[GetObject](../api/object.md#getobject-rpc)
[StreamGetObject](../api/object.md#streamgetobject-rpc) | [Vald Object APIs](../api/object.md) |
diff --git a/docs/api/build_proto.md b/docs/api/build_proto.md index d56fd25e07..8057b81cf1 100644 --- a/docs/api/build_proto.md +++ b/docs/api/build_proto.md @@ -12,8 +12,8 @@ If you can use one of the SDKs we recommend using it. Vald defines the proto file for each API. Let's check the below table for the details. -| API service name | proto | dependencies | usage | -| :--------------: | :---------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------- | +| API service name | proto | dependencies | usage | +| :--------------: | :-------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------- | | Insert | [insert.proto](https://github.com/vdaas/vald/blob/main/apis/proto/v1/vald/insert.proto) | [payload.proto](https://github.com/vdaas/vald/blob/main/apis/proto/v1/payload/payload.proto)
[annotation.proto](https://github.com/googleapis/googleapis/blob/master/google/api/annotations.proto) | Insert vectors into Vald Agent | | Update | [update.proto](https://github.com/vdaas/vald/blob/main/apis/proto/v1/vald/update.proto) | [payload.proto](https://github.com/vdaas/vald/blob/main/apis/proto/v1/payload/payload.proto)
[annotation.proto](https://github.com/googleapis/googleapis/blob/master/google/api/annotations.proto) | Update vectors stored in Vald Agent | | Upsert | [upsert.proto](https://github.com/vdaas/vald/blob/main/apis/proto/v1/vald/upsert.proto) | [payload.proto](https://github.com/vdaas/vald/blob/main/apis/proto/v1/payload/payload.proto)
[annotation.proto](https://github.com/googleapis/googleapis/blob/master/google/api/annotations.proto) | Update vectors stored Vald Agent or Insert vectors into Vald Agent | diff --git a/docs/api/filter-gateway.md b/docs/api/filter-gateway.md index 990dddbf22..09bb0aad94 100644 --- a/docs/api/filter-gateway.md +++ b/docs/api/filter-gateway.md @@ -54,26 +54,26 @@ service Filter { uint32 port = 2; } - message Config { - repeated Target targets = 1; + message Config { + repeated Target targets = 1; } } ``` - Insert.ObjectRequest - | field | type | label | required | desc. | + | field | type | label | required | desc. | | :--------: | :------------ | :---- | :------: | :-------------------------------------- | - | object | Object.Blob | | \* | the binary object to be inserted | - | config | Config | | \* | the configuration of the insert request | + | object | Object.Blob | | \* | the binary object to be inserted | + | config | Config | | \* | the configuration of the insert request | | vectorizer | Filter.Target | | \* | filter target | - Object.Blob - | field | type | label | required | desc. | - | :--------: | :------------ | :---- | :------: | :---------------- | - | id | string | | \* | the object ID | - | object | bytes | | \* | the binary object | + | field | type | label | required | desc. | + | :----: | :----- | :---- | :------: | :---------------- | + | id | string | | \* | the object ID | + | object | bytes | | \* | the binary object | - Insert.Config @@ -85,16 +85,16 @@ service Filter { - Filter.Target - | field | type | label | required | desc. | - | :-------: | :------- | :---- | :------: | :------------------- | - | host | string | | \* | the target hostname | - | port | port | | \* | the target port | + | field | type | label | required | desc. | + | :---: | :----- | :---- | :------: | :------------------ | + | host | string | | \* | the target hostname | + | port | port | | \* | the target port | - Filter.Config - | field | type | label | required | desc. | - | :-------: | :------------ | :----------------------------- | :------: | :------------------------------- | - | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | + | field | type | label | required | desc. | + | :-----: | :------------ | :----------------------------- | :------: | :------------------------------ | + | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | ### Output @@ -174,26 +174,26 @@ service Filter { uint32 port = 2; } - message Config { - repeated Target targets = 1; + message Config { + repeated Target targets = 1; } } ``` - Insert.ObjectRequest - | field | type | label | required | desc. | + | field | type | label | required | desc. | | :--------: | :------------ | :---- | :------: | :-------------------------------------- | - | object | Object.Blob | | \* | the binary object to be inserted | - | config | Config | | \* | the configuration of the insert request | + | object | Object.Blob | | \* | the binary object to be inserted | + | config | Config | | \* | the configuration of the insert request | | vectorizer | Filter.Target | | \* | filter configurations | - Object.Blob - | field | type | label | required | desc. | - | :--------: | :------------ | :---- | :------: | :---------------- | - | id | string | | \* | the object ID | - | object | bytes | | \* | the binary object | + | field | type | label | required | desc. | + | :----: | :----- | :---- | :------: | :---------------- | + | id | string | | \* | the object ID | + | object | bytes | | \* | the binary object | - Insert.Config @@ -205,16 +205,16 @@ service Filter { - Filter.Target - | field | type | label | required | desc. | - | :-------: | :------- | :---- | :------: | :------------------- | - | host | string | | \* | the target hostname | - | port | port | | \* | the target port | + | field | type | label | required | desc. | + | :---: | :----- | :---- | :------: | :------------------ | + | host | string | | \* | the target hostname | + | port | port | | \* | the target port | - Filter.Config - | field | type | label | required | desc. | - | :-------: | :------------ | :----------------------------- | :------: | :------------------------------- | - | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | + | field | type | label | required | desc. | + | :-----: | :------------ | :----------------------------- | :------: | :------------------------------ | + | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | ### Output @@ -292,7 +292,7 @@ service Filter { ```rpc message Insert { message MultiObjectRequest { - repeated ObjectRequest requests = 1; + repeated ObjectRequest requests = 1; } message ObjectRequest { @@ -321,32 +321,32 @@ service Filter { uint32 port = 2; } - message Config { - repeated Target targets = 1; + message Config { + repeated Target targets = 1; } } ``` - Insert.MultiObjectRequest - | field | type | label | required | desc. | - | :--------: | :------------ | :------------------------------------ | :------: | :--------------------------------------------------- | - | requests | ObjectRequest | repeated(Array[Insert.ObjectRequest]) | \* | the multiple search by binary object request content | + | field | type | label | required | desc. | + | :------: | :------------ | :------------------------------------ | :------: | :--------------------------------------------------- | + | requests | ObjectRequest | repeated(Array[Insert.ObjectRequest]) | \* | the multiple search by binary object request content | - Insert.ObjectRequest - | field | type | label | required | desc. | + | field | type | label | required | desc. | | :--------: | :------------ | :---- | :------: | :-------------------------------------- | - | object | Object.Blob | | \* | the binary object to be inserted | - | config | Config | | \* | the configuration of the insert request | + | object | Object.Blob | | \* | the binary object to be inserted | + | config | Config | | \* | the configuration of the insert request | | vectorizer | Filter.Target | | \* | filter configurations | - Object.Blob - | field | type | label | required | desc. | - | :--------: | :------------ | :---- | :------: | :---------------- | - | id | string | | \* | the object ID | - | object | bytes | | \* | the binary object | + | field | type | label | required | desc. | + | :----: | :----- | :---- | :------: | :---------------- | + | id | string | | \* | the object ID | + | object | bytes | | \* | the binary object | - Insert.Config @@ -358,16 +358,16 @@ service Filter { - Filter.Target - | field | type | label | required | desc. | - | :-------: | :------- | :---- | :------: | :------------------- | - | host | string | | \* | the target hostname | - | port | port | | \* | the target port | + | field | type | label | required | desc. | + | :---: | :----- | :---- | :------: | :------------------ | + | host | string | | \* | the target hostname | + | port | port | | \* | the target port | - Filter.Config - | field | type | label | required | desc. | - | :-------: | :------------ | :----------------------------- | :------: | :------------------------------- | - | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | + | field | type | label | required | desc. | + | :-----: | :------------ | :----------------------------- | :------: | :------------------------------ | + | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | ### Output @@ -456,47 +456,47 @@ service Filter { uint32 port = 2; } - message Config { - repeated Target targets = 1; + message Config { + repeated Target targets = 1; } } ``` - Update.ObjectRequest - | field | type | label | required | desc. | + | field | type | label | required | desc. | | :--------: | :------------ | :---- | :------: | :-------------------------------------- | - | object | Object.Blob | | \* | the binary object to be updated | - | config | Config | | \* | the configuration of the update request | + | object | Object.Blob | | \* | the binary object to be updated | + | config | Config | | \* | the configuration of the update request | | vectorizer | Filter.Target | | \* | filter target | - Object.Blob - | field | type | label | required | desc. | - | :--------: | :------------ | :---- | :------: | :---------------- | - | id | string | | \* | the object ID | - | object | bytes | | \* | the binary object | + | field | type | label | required | desc. | + | :----: | :----- | :---- | :------: | :---------------- | + | id | string | | \* | the object ID | + | object | bytes | | \* | the binary object | - Update.Config - | field | type | label | required | desc. | - | :---------------------: | :------------ | :---- | :------: | :--------------------------------------------------------------------------------------------------- | + | field | type | label | required | desc. | + | :---------------------: | :------------ | :---- | :------: | :-------------------------------------------------------------------------------------------------- | | skip_strict_exist_check | bool | | | check the same vector is already updated or not.
the ID should be unique if the value is `true`. | | timestamp | int64 | | | the timestamp of the vector updated.
if it is N/A, the current time will be used. | - | filters | Filter.Config | | | configuration for filter | + | filters | Filter.Config | | | configuration for filter | - Filter.Target - | field | type | label | required | desc. | - | :-------: | :------- | :---- | :------: | :------------------- | - | host | string | | \* | the target hostname | - | port | port | | \* | the target port | + | field | type | label | required | desc. | + | :---: | :----- | :---- | :------: | :------------------ | + | host | string | | \* | the target hostname | + | port | port | | \* | the target port | - Filter.Config - | field | type | label | required | desc. | - | :-------: | :------------ | :----------------------------- | :------: | :------------------------------- | - | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | + | field | type | label | required | desc. | + | :-----: | :------------ | :----------------------------- | :------: | :------------------------------ | + | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | ### Output @@ -514,8 +514,8 @@ service Filter { - Object.Location - | field | type | label | desc. | - | :---: | :----- | :---------------------- | :--------------------------------------------------------------------- | + | field | type | label | desc. | + | :---: | :----- | :---------------------- | :-------------------------------------------------------------------- | | name | string | | the name of vald agent pod where the request vector is updated. | | uuid | string | | the ID of the updated vector. It is the same as an `Object.Vector`. | | ips | string | repeated(Array[string]) | the IP list of `vald-agent` pods where the request vector is updated. | @@ -575,47 +575,47 @@ service Filter { uint32 port = 2; } - message Config { - repeated Target targets = 1; + message Config { + repeated Target targets = 1; } } ``` - Update.ObjectRequest - | field | type | label | required | desc. | + | field | type | label | required | desc. | | :--------: | :------------ | :---- | :------: | :-------------------------------------- | - | object | Object.Blob | | \* | the binary object to be updated | - | config | Config | | \* | the configuration of the update request | + | object | Object.Blob | | \* | the binary object to be updated | + | config | Config | | \* | the configuration of the update request | | vectorizer | Filter.Target | | \* | filter target | - Object.Blob - | field | type | label | required | desc. | - | :--------: | :------------ | :---- | :------: | :---------------- | - | id | string | | \* | the object ID | - | object | bytes | | \* | the binary object | + | field | type | label | required | desc. | + | :----: | :----- | :---- | :------: | :---------------- | + | id | string | | \* | the object ID | + | object | bytes | | \* | the binary object | - Update.Config - | field | type | label | required | desc. | - | :---------------------: | :------------ | :---- | :------: | :--------------------------------------------------------------------------------------------------- | + | field | type | label | required | desc. | + | :---------------------: | :------------ | :---- | :------: | :-------------------------------------------------------------------------------------------------- | | skip_strict_exist_check | bool | | | check the same vector is already updated or not.
the ID should be unique if the value is `true`. | | timestamp | int64 | | | the timestamp of the vector updated.
if it is N/A, the current time will be used. | - | filters | Filter.Config | | | configuration for filter | + | filters | Filter.Config | | | configuration for filter | - Filter.Target - | field | type | label | required | desc. | - | :-------: | :------- | :---- | :------: | :------------------- | - | host | string | | \* | the target hostname | - | port | port | | \* | the target port | + | field | type | label | required | desc. | + | :---: | :----- | :---- | :------: | :------------------ | + | host | string | | \* | the target hostname | + | port | port | | \* | the target port | - Filter.Config - | field | type | label | required | desc. | - | :-------: | :------------ | :----------------------------- | :------: | :------------------------------- | - | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | + | field | type | label | required | desc. | + | :-----: | :------------ | :----------------------------- | :------: | :------------------------------ | + | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | ### Output @@ -647,11 +647,11 @@ service Filter { - Object.Location - | field | type | label | desc. | - | :---: | :----- | :---------------------- | :--------------------------------------------------------------------- | - | name | string | | the name of vald agent pod where the request vector is updated . | - | uuid | string | | the ID of the updated vector. It is the same as an `Object.Vector`. | - | ips | string | repeated(Array[string]) | the IP list of `vald-agent` pods where the request vector is updated. | + | field | type | label | desc. | + | :---: | :----- | :---------------------- | :-------------------------------------------------------------------- | + | name | string | | the name of vald agent pod where the request vector is updated . | + | uuid | string | | the ID of the updated vector. It is the same as an `Object.Vector`. | + | ips | string | repeated(Array[string]) | the IP list of `vald-agent` pods where the request vector is updated. | - [google.rpc.Status](https://github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) @@ -727,8 +727,8 @@ service Filter { uint32 port = 2; } - message Config { - repeated Target targets = 1; + message Config { + repeated Target targets = 1; } } ``` @@ -741,39 +741,39 @@ service Filter { - Update.ObjectRequest - | field | type | label | required | desc. | + | field | type | label | required | desc. | | :--------: | :------------ | :---- | :------: | :-------------------------------------- | - | object | Object.Blob | | \* | the binary object to be updated | - | config | Config | | \* | the configuration of the update request | + | object | Object.Blob | | \* | the binary object to be updated | + | config | Config | | \* | the configuration of the update request | | vectorizer | Filter.Target | | \* | filter target | - Object.Blob - | field | type | label | required | desc. | - | :--------: | :------------ | :---- | :------: | :---------------- | - | id | string | | \* | the object ID | - | object | bytes | | \* | the binary object | + | field | type | label | required | desc. | + | :----: | :----- | :---- | :------: | :---------------- | + | id | string | | \* | the object ID | + | object | bytes | | \* | the binary object | - Update.Config - | field | type | label | required | desc. | - | :---------------------: | :------------ | :---- | :------: | :--------------------------------------------------------------------------------------------------- | + | field | type | label | required | desc. | + | :---------------------: | :------------ | :---- | :------: | :-------------------------------------------------------------------------------------------------- | | skip_strict_exist_check | bool | | | check the same vector is already updated or not.
the ID should be unique if the value is `true`. | | timestamp | int64 | | | the timestamp of the vector updated.
if it is N/A, the current time will be used. | - | filters | Filter.Config | | | configuration for filter | + | filters | Filter.Config | | | configuration for filter | - Filter.Target - | field | type | label | required | desc. | - | :-------: | :------- | :---- | :------: | :------------------- | - | host | string | | \* | the target hostname | - | port | port | | \* | the target port | + | field | type | label | required | desc. | + | :---: | :----- | :---- | :------: | :------------------ | + | host | string | | \* | the target hostname | + | port | port | | \* | the target port | - Filter.Config - | field | type | label | required | desc. | - | :-------: | :------------ | :----------------------------- | :------: | :------------------------------- | - | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | + | field | type | label | required | desc. | + | :-----: | :------------ | :----------------------------- | :------: | :------------------------------ | + | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | ### Output @@ -799,8 +799,8 @@ service Filter { - Object.Location - | field | type | label | desc. | - | :---: | :----- | :---------------------- | :--------------------------------------------------------------------- | + | field | type | label | desc. | + | :---: | :----- | :---------------------- | :-------------------------------------------------------------------- | | name | string | | the name of vald agent pod where the request vector is updated. | | uuid | string | | the ID of the updated vector. It is the same as an `Object.Vector`. | | ips | string | repeated(Array[string]) | the IP list of `vald-agent` pods where the request vector is updated. | @@ -862,26 +862,26 @@ service Filter { uint32 port = 2; } - message Config { - repeated Target targets = 1; + message Config { + repeated Target targets = 1; } } ``` - Upsert.ObjectRequest - | field | type | label | required | desc. | - | :--------: | :------------ | :---- | :------: | :--------------------------------------- | - | object | Object.Blob | | \* | the binary object to be upserted | - | config | Config | | \* | the configuration of the upsert request | - | vectorizer | Filter.Target | | \* | filter target | + | field | type | label | required | desc. | + | :--------: | :------------ | :---- | :------: | :-------------------------------------- | + | object | Object.Blob | | \* | the binary object to be upserted | + | config | Config | | \* | the configuration of the upsert request | + | vectorizer | Filter.Target | | \* | filter target | - Object.Blob - | field | type | label | required | desc. | - | :--------: | :------------ | :---- | :------: | :---------------- | - | id | string | | \* | the object ID | - | object | bytes | | \* | the binary object | + | field | type | label | required | desc. | + | :----: | :----- | :---- | :------: | :---------------- | + | id | string | | \* | the object ID | + | object | bytes | | \* | the binary object | - Update.Config @@ -893,16 +893,16 @@ service Filter { - Filter.Target - | field | type | label | required | desc. | - | :-------: | :------- | :---- | :------: | :------------------- | - | host | string | | \* | the target hostname | - | port | port | | \* | the target port | + | field | type | label | required | desc. | + | :---: | :----- | :---- | :------: | :------------------ | + | host | string | | \* | the target hostname | + | port | port | | \* | the target port | - Filter.Config - | field | type | label | required | desc. | - | :-------: | :------------ | :----------------------------- | :------: | :------------------------------- | - | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | + | field | type | label | required | desc. | + | :-----: | :------------ | :----------------------------- | :------: | :------------------------------ | + | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | ### Output @@ -978,26 +978,26 @@ service Filter { uint32 port = 2; } - message Config { - repeated Target targets = 1; + message Config { + repeated Target targets = 1; } } ``` - Upsert.ObjectRequest - | field | type | label | required | desc. | - | :--------: | :------------ | :---- | :------: | :--------------------------------------- | - | object | Object.Blob | | \* | the binary object to be upserted | - | config | Config | | \* | the configuration of the upsert request | - | vectorizer | Filter.Target | | \* | filter target | + | field | type | label | required | desc. | + | :--------: | :------------ | :---- | :------: | :-------------------------------------- | + | object | Object.Blob | | \* | the binary object to be upserted | + | config | Config | | \* | the configuration of the upsert request | + | vectorizer | Filter.Target | | \* | filter target | - Object.Blob - | field | type | label | required | desc. | - | :--------: | :------------ | :---- | :------: | :---------------- | - | id | string | | \* | the object ID | - | object | bytes | | \* | the binary object | + | field | type | label | required | desc. | + | :----: | :----- | :---- | :------: | :---------------- | + | id | string | | \* | the object ID | + | object | bytes | | \* | the binary object | - Update.Config @@ -1009,17 +1009,17 @@ service Filter { - Filter.Target - | field | type | label | required | desc. | - | :-------: | :------- | :---- | :------: | :------------------- | - | host | string | | \* | the target hostname | - | port | port | | \* | the target port | + | field | type | label | required | desc. | + | :---: | :----- | :---- | :------: | :------------------ | + | host | string | | \* | the target hostname | + | port | port | | \* | the target port | - Filter.Config - | field | type | label | required | desc. | - | :-------: | :------------ | :----------------------------- | :------: | :------------------------------- | - | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | - + | field | type | label | required | desc. | + | :-----: | :------------ | :----------------------------- | :------: | :------------------------------ | + | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | + ### Output - the scheme of `payload.v1.Object.StreamLocation` @@ -1130,8 +1130,8 @@ service Filter { uint32 port = 2; } - message Config { - repeated Target targets = 1; + message Config { + repeated Target targets = 1; } } ``` @@ -1144,18 +1144,18 @@ service Filter { - Upsert.ObjectRequest - | field | type | label | required | desc. | - | :--------: | :------------ | :---- | :------: | :--------------------------------------- | - | object | Object.Blob | | \* | the binary object to be upserted | - | config | Config | | \* | the configuration of the upsert request | - | vectorizer | Filter.Target | | \* | filter target | + | field | type | label | required | desc. | + | :--------: | :------------ | :---- | :------: | :-------------------------------------- | + | object | Object.Blob | | \* | the binary object to be upserted | + | config | Config | | \* | the configuration of the upsert request | + | vectorizer | Filter.Target | | \* | filter target | - Object.Blob - | field | type | label | required | desc. | - | :--------: | :------------ | :---- | :------: | :---------------- | - | id | string | | \* | the object ID | - | object | bytes | | \* | the binary object | + | field | type | label | required | desc. | + | :----: | :----- | :---- | :------: | :---------------- | + | id | string | | \* | the object ID | + | object | bytes | | \* | the binary object | - Update.Config @@ -1167,16 +1167,16 @@ service Filter { - Filter.Target - | field | type | label | required | desc. | - | :-------: | :------- | :---- | :------: | :------------------- | - | host | string | | \* | the target hostname | - | port | port | | \* | the target port | + | field | type | label | required | desc. | + | :---: | :----- | :---- | :------: | :------------------ | + | host | string | | \* | the target hostname | + | port | port | | \* | the target port | - Filter.Config - | field | type | label | required | desc. | - | :-------: | :------------ | :----------------------------- | :------: | :------------------------------- | - | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | + | field | type | label | required | desc. | + | :-----: | :------------ | :----------------------------- | :------: | :------------------------------ | + | targets | Filter.Target | repeated(Array[Filter.Target]) | \* | the filter target configuration | ### Output @@ -1263,32 +1263,32 @@ service Filter { uint32 port = 2; } - message Config { - repeated Target targets = 1; + message Config { + repeated Target targets = 1; } } ``` - Search.ObjectRequest - | field | type | label | required | desc. | - | :----: | :---------------- | :----- | :------: | :------------------------------------------------------ | - | object | bytes | | \* | the binary object to be searched | - | config | Config | | \* | the configuration of the search request | - | vectorizer | Filter.Target | | \* | filter target | + | field | type | label | required | desc. | + | :--------: | :------------ | :---- | :------: | :-------------------------------------- | + | object | bytes | | \* | the binary object to be searched | + | config | Config | | \* | the configuration of the search request | + | vectorizer | Filter.Target | | \* | filter target | - Search.Config - | field | type | label | required | desc. | - | :-------------: | :------------ | :---- | :-----------------------------------------: | :---------------------------------------------------- | - | request_id | string | | | unique request ID | - | num | uint32 | | \* | the maximum number of result to be returned | - | radius | float | | \* | the search radius | - | epsilon | float | | \* | the search coefficient (default value is `0.1`) | - | timeout | int64 | | | Search timeout in nanoseconds (default value is `5s`) | - | ingress_filters | Filter.Config | | | Ingress Filter configuration | - | egress_filters | Filter.Config | | | Egress Filter configuration | - | min_num | uint32 | | | the minimum number of result to be returned | + | field | type | label | required | desc. | + | :-------------: | :------------ | :---- | :------: | :---------------------------------------------------- | + | request_id | string | | | unique request ID | + | num | uint32 | | \* | the maximum number of result to be returned | + | radius | float | | \* | the search radius | + | epsilon | float | | \* | the search coefficient (default value is `0.1`) | + | timeout | int64 | | | Search timeout in nanoseconds (default value is `5s`) | + | ingress_filters | Filter.Config | | | Ingress Filter configuration | + | egress_filters | Filter.Config | | | Egress Filter configuration | + | min_num | uint32 | | | the minimum number of result to be returned | ### Output @@ -1376,32 +1376,32 @@ service Filter { uint32 port = 2; } - message Config { - repeated Target targets = 1; + message Config { + repeated Target targets = 1; } } ``` - Search.ObjectRequest - | field | type | label | required | desc. | - | :----: | :---------------- | :----- | :------: | :------------------------------------------------------ | - | object | bytes | | \* | the binary object to be searched | - | config | Config | | \* | the configuration of the search request | - | vectorizer | Filter.Target | | \* | filter target | + | field | type | label | required | desc. | + | :--------: | :------------ | :---- | :------: | :-------------------------------------- | + | object | bytes | | \* | the binary object to be searched | + | config | Config | | \* | the configuration of the search request | + | vectorizer | Filter.Target | | \* | filter target | - Search.Config - | field | type | label | required | desc. | - | :-------------: | :------------ | :---- | :-----------------------------------------: | :---------------------------------------------------- | - | request_id | string | | | unique request ID | - | num | uint32 | | \* | the maximum number of result to be returned | - | radius | float | | \* | the search radius | - | epsilon | float | | \* | the search coefficient (default value is `0.1`) | - | timeout | int64 | | | Search timeout in nanoseconds (default value is `5s`) | - | ingress_filters | Filter.Config | | | Ingress Filter configuration | - | egress_filters | Filter.Config | | | Egress Filter configuration | - | min_num | uint32 | | | the minimum number of result to be returned | + | field | type | label | required | desc. | + | :-------------: | :------------ | :---- | :------: | :---------------------------------------------------- | + | request_id | string | | | unique request ID | + | num | uint32 | | \* | the maximum number of result to be returned | + | radius | float | | \* | the search radius | + | epsilon | float | | \* | the search coefficient (default value is `0.1`) | + | timeout | int64 | | | Search timeout in nanoseconds (default value is `5s`) | + | ingress_filters | Filter.Config | | | Ingress Filter configuration | + | egress_filters | Filter.Config | | | Egress Filter configuration | + | min_num | uint32 | | | the minimum number of result to be returned | ### Output @@ -1478,8 +1478,8 @@ service Filter { ```rpc message Search { - message MultiObjectRequest { - repeated ObjectRequest requests = 1; + message MultiObjectRequest { + repeated ObjectRequest requests = 1; } message ObjectRequest { @@ -1506,8 +1506,8 @@ service Filter { uint32 port = 2; } - message Config { - repeated Target targets = 1; + message Config { + repeated Target targets = 1; } } ``` @@ -1520,24 +1520,24 @@ service Filter { - Search.ObjectRequest - | field | type | label | required | desc. | - | :----: | :---------------- | :----- | :------: | :------------------------------------------------------ | - | object | bytes | | \* | the binary object to be searched | - | config | Config | | \* | the configuration of the search request | - | vectorizer | Filter.Target | | \* | filter target | + | field | type | label | required | desc. | + | :--------: | :------------ | :---- | :------: | :-------------------------------------- | + | object | bytes | | \* | the binary object to be searched | + | config | Config | | \* | the configuration of the search request | + | vectorizer | Filter.Target | | \* | filter target | - Search.Config - | field | type | label | required | desc. | - | :-------------: | :------------ | :---- | :-----------------------------------------: | :---------------------------------------------------- | - | request_id | string | | | unique request ID | - | num | uint32 | | \* | the maximum number of result to be returned | - | radius | float | | \* | the search radius | - | epsilon | float | | \* | the search coefficient (default value is `0.1`) | - | timeout | int64 | | | Search timeout in nanoseconds (default value is `5s`) | - | ingress_filters | Filter.Config | | | Ingress Filter configuration | - | egress_filters | Filter.Config | | | Egress Filter configuration | - | min_num | uint32 | | | the minimum number of result to be returned | + | field | type | label | required | desc. | + | :-------------: | :------------ | :---- | :------: | :---------------------------------------------------- | + | request_id | string | | | unique request ID | + | num | uint32 | | \* | the maximum number of result to be returned | + | radius | float | | \* | the search radius | + | epsilon | float | | \* | the search coefficient (default value is `0.1`) | + | timeout | int64 | | | Search timeout in nanoseconds (default value is `5s`) | + | ingress_filters | Filter.Config | | | Ingress Filter configuration | + | egress_filters | Filter.Config | | | Egress Filter configuration | + | min_num | uint32 | | | the minimum number of result to be returned | ### Output @@ -1590,4 +1590,4 @@ service Filter { | 0 | OK | | 3 | INVALID_ARGUMENT | | 4 | DEADLINE_EXCEEDED | -| 5 | NOT_FOUND | \ No newline at end of file +| 5 | NOT_FOUND | diff --git a/docs/contributing/coding-style.md b/docs/contributing/coding-style.md index 0ecc4af0ac..da6644f1c5 100644 --- a/docs/contributing/coding-style.md +++ b/docs/contributing/coding-style.md @@ -1160,7 +1160,7 @@ Still, in some cases, you may need to change the generated code to meet your req ngt: test.fields.ngt, eg: test.fields.eg, streamConcurrency: test.fields.streamConcurrency, - // we may have more fields defined in the struct, + // we may have more fields defined in the struct, // and we need to set them all here } @@ -1225,7 +1225,7 @@ The generated test case will enable these two parallel modes by default. It is i func Test_server_CreateIndex(t *testing.T) { t.Parallel() // parallel for the test function type args struct { - + ... for _, tc := range tests { diff --git a/docs/overview/about-vald.md b/docs/overview/about-vald.md index 8250bf2b89..dc1fb00b39 100644 --- a/docs/overview/about-vald.md +++ b/docs/overview/about-vald.md @@ -5,6 +5,7 @@ This document gives an overview of what is Vald and what you can do with Vald. ## What is Vald? + Vald is a highly scalable distributed fast approximate nearest neighbor dense vector search engine. Vald is designed and implemented based on Cloud-Native architecture. @@ -18,38 +19,47 @@ Vald is easy to use, feature-rich and highly customizable as you needed. ### What Vald can do? + - Asynchronous Auto Indexing - - Usually the graph requires locking during indexing, which causes stop-the-world. But Vald uses distributed index graphs so it continues to work during indexing. + + - Usually the graph requires locking during indexing, which causes stop-the-world. But Vald uses distributed index graphs so it continues to work during indexing. - Customizable Ingress/Egress Filtering - - Vald implements it's own highly customizable Ingress/Egress filter. - - Which can be configured to fit the gRPC interface. - - Ingress Filter: Ability to Vectorize through filter on request. - - Egress Filter: rerank or filter the searching result with your own algorithm. + + - Vald implements it's own highly customizable Ingress/Egress filter. + - Which can be configured to fit the gRPC interface. + - Ingress Filter: Ability to Vectorize through filter on request. + - Egress Filter: rerank or filter the searching result with your own algorithm. - Cloud-native based vector searching engine - - Horizontal scalable on memory and CPU for your demand. + + - Horizontal scalable on memory and CPU for your demand. - Auto Backup for Index data - - Vald supports to backup Vald Agent index data using Object Storage or Persistent Volume. + + - Vald supports to backup Vald Agent index data using Object Storage or Persistent Volume. - Distributed Indexing - - Vald distributes vector index to multiple agents, and each agent stores different index. + + - Vald distributes vector index to multiple agents, and each agent stores different index. - Index Replication - - Vald stores each index in multiple agents which enables index replicas. - - Automatically rebalancing the replica when some Vald agent goes down. + + - Vald stores each index in multiple agents which enables index replicas. + - Automatically rebalancing the replica when some Vald agent goes down. - Easy to use - - Vald can be easily installed in a few steps. + + - Vald can be easily installed in a few steps. - Highly customizable - - You can configure the number of vector dimensions, the number of replica and etc. + + - You can configure the number of vector dimensions, the number of replica and etc. - Multi language supported - - Go, Java, Clojure, Node.js, and Python client library are supported. - - gRPC APIs can be triggered by any programming languages which support gRPC. - - REST API is also supported. + - Go, Java, Clojure, Node.js, and Python client library are supported. + - gRPC APIs can be triggered by any programming languages which support gRPC. + - REST API is also supported. ### Use cases diff --git a/docs/overview/architecture.md b/docs/overview/architecture.md index 39d76107e3..13149194c7 100644 --- a/docs/overview/architecture.md +++ b/docs/overview/architecture.md @@ -14,20 +14,20 @@ Vald is based on the following technologies. - [Kubernetes](https://kubernetes.io/) - To easily scale and manage Vald, it is used by deploying and running on [Kubernetes](https://kubernetes.io/). - Vald takes all of the advantages of using Kubernetes. - For more details please read the [next section](#concept). + To easily scale and manage Vald, it is used by deploying and running on [Kubernetes](https://kubernetes.io/). + Vald takes all of the advantages of using Kubernetes. + For more details please read the [next section](#concept). - [Helm](https://helm.sh/) - Helm helps you to deploy and configure Vald. - Vald contains multiple components and configurations. - Helm helps us to manage those manifests and provides a better and easy way to deploy and configure Vald. + Helm helps you to deploy and configure Vald. + Vald contains multiple components and configurations. + Helm helps us to manage those manifests and provides a better and easy way to deploy and configure Vald. - [NGT](https://github.com/yahoojapan/NGT) - NGT is one of the core components of Vald. - NGT is a super-fast vector search engine used by Vald to guarantee the high performance of Vald. + NGT is one of the core components of Vald. + NGT is a super-fast vector search engine used by Vald to guarantee the high performance of Vald. ### Concept diff --git a/docs/overview/component/README.md b/docs/overview/component/README.md index 4cc4a6ff1c..49461c6b9b 100644 --- a/docs/overview/component/README.md +++ b/docs/overview/component/README.md @@ -44,6 +44,7 @@ This component can reorder the searching result from multiple Vald Agents based ### Vald Filter Gateway Vald Filter Gateway forwards the request to Vald Ingress Filter before processing it and forwards the response to the Vald Egress Filter before returning the searching result to the user. + ## Vald Load Balancing Load balancing is one of the important concepts in distributed computing, which means it distributes a set of tasks over a set of resources aiming for making the overall processing more efficient. diff --git a/docs/overview/component/discoverer.md b/docs/overview/component/discoverer.md index 8d3802ef41..691dca6308 100644 --- a/docs/overview/component/discoverer.md +++ b/docs/overview/component/discoverer.md @@ -40,7 +40,6 @@ The kind of metrics are here: | CPU | CPU limit and CPU request | | Memory | Memory limit and Memory request | - When syncing success, Vald Discoverer chooses the necessary metrics from the result and stores them into their four kinds of `Map` on their local memory: | Map name | Description | diff --git a/docs/overview/component/filter-gateway.md b/docs/overview/component/filter-gateway.md index b97516d68d..1561363016 100644 --- a/docs/overview/component/filter-gateway.md +++ b/docs/overview/component/filter-gateway.md @@ -105,13 +105,12 @@ Vector filtering allows you to add the process: for example, to remove different If you want to use this feature, please deploy your own egress filter component, which meets the following interface. - - The scheme of egress filter service ```rpc // https://github.com/vdaas/vald/blob/main/apis/proto/v1/filter/ingress/egress_filter.proto service Filter { - + // Represent the RPC to filter the distance. rpc FilterDistance(payload.v1.Object.Distance) returns (payload.v1.Object.Distance) { @@ -120,7 +119,7 @@ If you want to use this feature, please deploy your own egress filter component, body : "*" }; } - + // Represent the RPC to filter the vector. rpc FilterVector(payload.v1.Object.Vector) returns (payload.v1.Object.Vector) { @@ -143,7 +142,7 @@ If you want to use this feature, please deploy your own egress filter component, // The distance. float distance = 2; } - + // Represent a vector. message Vector { // The vector ID. diff --git a/docs/overview/component/index-manager.md b/docs/overview/component/index-manager.md index 432869c89f..edc59215ec 100644 --- a/docs/overview/component/index-manager.md +++ b/docs/overview/component/index-manager.md @@ -40,4 +40,5 @@ Vald Index Manager runs this process periodically by set time intervals. Concurrency means the number of Vald Agent pods for simultaneously sending requests for the indexing operation.
When the Vald Agent pod has no uncommitted index or is running the indexing function already, it does not send the request. + diff --git a/docs/support/contacts.md b/docs/support/contacts.md index d0102af6ad..87b1c042c2 100644 --- a/docs/support/contacts.md +++ b/docs/support/contacts.md @@ -7,8 +7,8 @@ Vald is an open source project and we would like to hear from you. If you are interested in Vald, or you have any questions about Vald, please feel free to contact us. We recommend communicating on slack, but using E-mail is also welcome. - -Slack: [Vald Community](https://join.slack.com/t/vald-community/shared_invite/zt-db2ky9o4-R_9p2sVp8xRwztVa8gfnPA) + +Slack: [Vald Community](https://join.slack.com/t/vald-community/shared_invite/zt-db2ky9o4-R_9p2sVp8xRwztVa8gfnPA) E-mail: [vald@vdaas.org](mailto:vald@vdaas.org) diff --git a/docs/support/faq.md b/docs/support/faq.md index 833a80db27..7d22acc470 100644 --- a/docs/support/faq.md +++ b/docs/support/faq.md @@ -75,6 +75,7 @@ There are three ways to backup index data: 1. Using the external storage and Persistent Volume + Please refer to [the sample configurations](https://github.com/vdaas/vald/tree/main/charts/vald/values). --- diff --git a/docs/troubleshooting/client-side.md b/docs/troubleshooting/client-side.md index a0e49c8bfe..c59e60d1e8 100644 --- a/docs/troubleshooting/client-side.md +++ b/docs/troubleshooting/client-side.md @@ -27,16 +27,16 @@ There are two possible reasons. 1. Indexing has not finished in Vald Agent - Vald will search the nearest vectors of query from the indexing in Vald Agent. - If the indexing process is running, Vald Agent returns no search result. - - It will resolve when completed indexing instructions, like `CreateIndex`. + Vald will search the nearest vectors of query from the indexing in Vald Agent. + If the indexing process is running, Vald Agent returns no search result. + + It will resolve when completed indexing instructions, like `CreateIndex`. 1. Too short timeout for searching - When the search timeout configuration is too short, Vald LB Gateway stops the searching process before getting the search result from Vald Agent. + When the search timeout configuration is too short, Vald LB Gateway stops the searching process before getting the search result from Vald Agent. - In the sense of search operation, you can modify search timeout by [payload config](../api/search.md). + In the sense of search operation, you can modify search timeout by [payload config](../api/search.md).
It is easy to find out which problem occurs by inspections of the log of each Pod, like stern.
diff --git a/docs/troubleshooting/provisioning.md b/docs/troubleshooting/provisioning.md
index 6df03d665c..f19fe942f9 100644
--- a/docs/troubleshooting/provisioning.md
+++ b/docs/troubleshooting/provisioning.md
@@ -13,4 +13,3 @@ If any components output the `INTERNAL` error log, let us know the detail.
## Related Document
- [Client Side Troubleshooting](../troubleshooting/client-side.md)
-
diff --git a/docs/tutorial/get-started.md b/docs/tutorial/get-started.md
index 6468404927..ec8b8addea 100644
--- a/docs/tutorial/get-started.md
+++ b/docs/tutorial/get-started.md
@@ -407,21 +407,21 @@ If you are interested, please refer to [SDKs](../user-guides/sdks.md).
- Remove 400 indexed training datasets from the Vald agent.
- ```go - for i := range ids [:insertCount] { - _, err := client.Remove(ctx, &payload.Remove_Request{ - Id: &payload.Object_ID{ - Id: ids[i], - }, - }) - if err != nil { - glg.Fatal(err) - } - if i%10 == 0 { - glg.Infof("Removed %d", i) - } - } - ``` + ```go + for i := range ids [:insertCount] { + _, err := client.Remove(ctx, &payload.Remove_Request{ + Id: &payload.Object_ID{ + Id: ids[i], + }, + }) + if err != nil { + glg.Fatal(err) + } + if i%10 == 0 { + glg.Infof("Removed %d", i) + } + } + ```
diff --git a/docs/tutorial/vald-agent-standalone-on-docker.md b/docs/tutorial/vald-agent-standalone-on-docker.md
index 8ca2e59c02..81debbf370 100644
--- a/docs/tutorial/vald-agent-standalone-on-docker.md
+++ b/docs/tutorial/vald-agent-standalone-on-docker.md
@@ -13,6 +13,7 @@ Fashion-MNIST is used as an example dataset, same as [Get Started](../tutorial/g
- libhdf5 (_only required for this tutorial._)
HDF5 is required for this tutorial. If hdf5 is not installed, please install [HDF5](https://www.hdfgroup.org/).
+
```bash @@ -25,6 +26,7 @@ apt-get install libhdf5-serial-dev # homebrew brew install hdf5 ``` +
## Deploy
@@ -34,174 +36,177 @@ This chapter will use NGT for the core engine of Vald Agent.
1. Clone the vdaas/vald repository
- ```bash
- git clone https://github.com/vdaas/vald.git
- ```
+ ```bash
+ git clone https://github.com/vdaas/vald.git
+ ```
1. Create a directory for setting deploy vald-agent-ngt
- ```bash
- cd vald && mkdir -p tutorial && cd tutorial
- ```
+ ```bash
+ cd vald && mkdir -p tutorial && cd tutorial
+ ```
1. Create the `config.yaml`
- The configuration of Vald agent for docker is set using `config.yaml`
- You can also check [the sample configuration](https://github.com/vdaas/vald/blob/main/cmd/agent/core/ngt/sample.yaml). - - ```bash - cat << EOF > config.yaml - --- - version: v0.0.0 - time_zone: JST - logging: - logger: glg - level: debug - format: raw - server_config: - servers: - - name: agent-grpc - host: 0.0.0.0 - port: 8081 - mode: GRPC - probe_wait_time: "3s" - http: - shutdown_duration: "5s" - handler_timeout: "" - idle_timeout: "" - read_header_timeout: "" - read_timeout: "" - write_timeout: "" - startup_strategy: - - agent-grpc - shutdown_strategy: - - agent-grpc - full_shutdown_duration: 600s - tls: - enabled: false - # cert: /path/to/cert - # key: /path/to/key - # ca: /path/to/ca - ngt: - # path to index data - index_path: "/etc/server/backup" - # vector dimension - dimension: 784 - # bulk insert chunk size - bulk_insert_chunk_size: 10 - # distance_type, which should be "l1", "l2" "angle", "hamming", "cosine", "normalizedangle", "normalizedcosine" or "jaccard" - distance_type: l2 - # object_type, which should be "float" or "uint8" - object_type: float - # creation edge size - creation_edge_size: 20 - # search edge size - search_edge_size: 10 - # The limit duration of automatic indexing - # auto_index_duration_limit should be 30m-6h for production use. Below setting is a just example - auto_index_duration_limit: 1m - # Check duration of automatic indexing. - # auto_index_check_duration be 10m-1h for production use. Below setting is a just example - auto_index_check_duration: 10s - # The number of cache to trigger automatic indexing - auto_index_length: 100 - # The limit duration of auto saving indexing - # auto_save_index_duration should be 30m-60m for production use. The below setting is a just example. - auto_save_index_duration: 90s - # The maximum limit duration for an initial delay - # initial_delay_max_duration should be 3m-5m for production use. The below setting is a just example. - initial_delay_max_duration: 60s - # The default create index batch pool size. - # When it is too large comparing to machine resource, the docker container will be crash. - default_pool_size: 500 - EOF - ``` + The configuration of Vald agent for docker is set using `config.yaml`
+ You can also check [the sample configuration](https://github.com/vdaas/vald/blob/main/cmd/agent/core/ngt/sample.yaml). + + ```bash + cat << EOF > config.yaml + --- + version: v0.0.0 + time_zone: JST + logging: + logger: glg + level: debug + format: raw + server_config: + servers: + - name: agent-grpc + host: 0.0.0.0 + port: 8081 + mode: GRPC + probe_wait_time: "3s" + http: + shutdown_duration: "5s" + handler_timeout: "" + idle_timeout: "" + read_header_timeout: "" + read_timeout: "" + write_timeout: "" + startup_strategy: + - agent-grpc + shutdown_strategy: + - agent-grpc + full_shutdown_duration: 600s + tls: + enabled: false + # cert: /path/to/cert + # key: /path/to/key + # ca: /path/to/ca + ngt: + # path to index data + index_path: "/etc/server/backup" + # vector dimension + dimension: 784 + # bulk insert chunk size + bulk_insert_chunk_size: 10 + # distance_type, which should be "l1", "l2" "angle", "hamming", "cosine", "normalizedangle", "normalizedcosine" or "jaccard" + distance_type: l2 + # object_type, which should be "float" or "uint8" + object_type: float + # creation edge size + creation_edge_size: 20 + # search edge size + search_edge_size: 10 + # The limit duration of automatic indexing + # auto_index_duration_limit should be 30m-6h for production use. Below setting is a just example + auto_index_duration_limit: 1m + # Check duration of automatic indexing. + # auto_index_check_duration be 10m-1h for production use. Below setting is a just example + auto_index_check_duration: 10s + # The number of cache to trigger automatic indexing + auto_index_length: 100 + # The limit duration of auto saving indexing + # auto_save_index_duration should be 30m-60m for production use. The below setting is a just example. + auto_save_index_duration: 90s + # The maximum limit duration for an initial delay + # initial_delay_max_duration should be 3m-5m for production use. The below setting is a just example. + initial_delay_max_duration: 60s + # The default create index batch pool size. + # When it is too large comparing to machine resource, the docker container will be crash. + default_pool_size: 500 + EOF + ``` 1. Create a backup directory - To avoid removing the indexing data due to any trouble after finishing indexing, we should prepare the path for auto backup. + To avoid removing the indexing data due to any trouble after finishing indexing, we should prepare the path for auto backup. - ```bash - mkdir -p backup - ``` + ```bash + mkdir -p backup + ``` 1. Deploy Vald Agent on Docker - To deploy Vald agent on docker with `config.yaml`, you can run the below command. - Note: - - Please check whether there is `config.yaml` file and `backup` directory in your current directory. + To deploy Vald agent on docker with `config.yaml`, you can run the below command. + Note: + + - Please check whether there is `config.yaml` file and `backup` directory in your current directory. - ```bash - docker run -v $(pwd):/etc/server -u "$(id -u $USER):$(id -g $USER)" -v /etc/passwd:/etc/passwd:ro -v /etc/group:/etc/group:ro -p 8081:8081 --rm -it vdaas/vald-agent-ngt - ``` + ```bash + docker run -v $(pwd):/etc/server -u "$(id -u $USER):$(id -g $USER)" -v /etc/passwd:/etc/passwd:ro -v /etc/group:/etc/group:ro -p 8081:8081 --rm -it vdaas/vald-agent-ngt + ``` 1. Verify - If the deployment success, you can confirm the output will be similar to below. + If the deployment success, you can confirm the output will be similar to below. - ```bash - 2020-07-01 03:02:41 [INFO]: maxprocs: Leaving GOMAXPROCS=4: CPU quota undefined - 2020-07-01 03:02:41 [INFO]: service agent ngt v0.0.0 starting... - 2020-07-01 03:02:41 [INFO]: executing daemon pre-start function - 2020-07-01 03:02:41 [INFO]: executing daemon start function - 2020-07-01 03:02:41 [INFO]: server agent-grpc executing preStartFunc - 2020-07-01 12:02:41 [INFO]: gRPC server agent-grpc starting on 0.0.0.0:8081 - ``` + ```bash + 2020-07-01 03:02:41 [INFO]: maxprocs: Leaving GOMAXPROCS=4: CPU quota undefined + 2020-07-01 03:02:41 [INFO]: service agent ngt v0.0.0 starting... + 2020-07-01 03:02:41 [INFO]: executing daemon pre-start function + 2020-07-01 03:02:41 [INFO]: executing daemon start function + 2020-07-01 03:02:41 [INFO]: server agent-grpc executing preStartFunc + 2020-07-01 12:02:41 [INFO]: gRPC server agent-grpc starting on 0.0.0.0:8081 + ``` ## Run using example code 1. Download dataset - This tutorial uses [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) as a dataset for indexing and search query. + This tutorial uses [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) as a dataset for indexing and search query. - ```bash - # move to the working directory - cd example/client/agent - ``` + ```bash + # move to the working directory + cd example/client/agent + ``` - ```bash - # download Fashion-MNIST testing dataset - wget http://ann-benchmarks.com/fashion-mnist-784-euclidean.hdf5 - ``` + ```bash + # download Fashion-MNIST testing dataset + wget http://ann-benchmarks.com/fashion-mnist-784-euclidean.hdf5 + ``` 1. Running example - Vald provides multiple language client libraries such as Go, Java, Node.js, Python, etc.
- This example will insert and index 400 vectors into the Vald from the Fashion-MNIST dataset via gRPC. - And then, after waiting for indexing, it will request to search the nearest vector 10 times. - You will get the 10 nearest neighbor vectors for each search query.
+ Vald provides multiple language client libraries such as Go, Java, Node.js, Python, etc.
+ This example will insert and index 400 vectors into the Vald from the Fashion-MNIST dataset via gRPC. + And then, after waiting for indexing, it will request to search the nearest vector 10 times. + You will get the 10 nearest neighbor vectors for each search query.
- We use [`example/client/agent/main.go`](https://github.com/vdaas/vald/blob/main/example/client/agent/main.go) to run the example. - The example code is the same as running an example-only Vald agent on Kubernetes. - If you want to learn the detail of running an example, please refer to the tutorial of [Vald Agent Standalone on kubernetes](../tutorial/get-started.md/#run-using-example-code). + We use [`example/client/agent/main.go`](https://github.com/vdaas/vald/blob/main/example/client/agent/main.go) to run the example. + The example code is the same as running an example-only Vald agent on Kubernetes. + If you want to learn the detail of running an example, please refer to the tutorial of [Vald Agent Standalone on kubernetes](../tutorial/get-started.md/#run-using-example-code). - ```bash - # run example - go run main.go - ``` + ```bash + # run example + go run main.go + ``` -
The 5 steps to Vald Agent Standalone on Kubernetes with Vald:
+
1. [Check and Satisfy the Requirements](#Requirements)
1. [Prepare Kubernetes Cluster](#Prepare-the-Kubernetes-Cluster)
1. [Deploy Vald Agent Standalone on Kubernetes cluster](#Deploy-Vald-Agent-Standalone-on-Kubernetes-Cluster)
@@ -59,9 +60,9 @@ brew install hdf5
1. Prepare Kubernetes cluster
- To complete get started, the Kubernetes cluster is required.- Remove 400 indexed training datasets from the Vald agent.
example code
- ```go - for i := range ids [:insertCount] { - _, err := client.Remove(ctx, &payload.Remove_Request{ - Id: &payload.Object_ID{ - Id: ids[i], - }, - }) - if err != nil { - glg.Fatal(err) - } - if i%10 == 0 { - glg.Infof("Removed %d", i) - } - } - ``` + ```go + for i := range ids [:insertCount] { + _, err := client.Remove(ctx, &payload.Remove_Request{ + Id: &payload.Object_ID{ + Id: ids[i], + }, + }) + if err != nil { + glg.Fatal(err) + } + if i%10 == 0 { + glg.Infof("Removed %d", i) + } + } + ```
[Optional] Install HDF5
```bash @@ -25,6 +26,7 @@ apt-get install libhdf5-serial-dev # homebrew brew install hdf5 ``` +
- You can also check [the sample configuration](https://github.com/vdaas/vald/blob/main/cmd/agent/core/ngt/sample.yaml). - - ```bash - cat << EOF > config.yaml - --- - version: v0.0.0 - time_zone: JST - logging: - logger: glg - level: debug - format: raw - server_config: - servers: - - name: agent-grpc - host: 0.0.0.0 - port: 8081 - mode: GRPC - probe_wait_time: "3s" - http: - shutdown_duration: "5s" - handler_timeout: "" - idle_timeout: "" - read_header_timeout: "" - read_timeout: "" - write_timeout: "" - startup_strategy: - - agent-grpc - shutdown_strategy: - - agent-grpc - full_shutdown_duration: 600s - tls: - enabled: false - # cert: /path/to/cert - # key: /path/to/key - # ca: /path/to/ca - ngt: - # path to index data - index_path: "/etc/server/backup" - # vector dimension - dimension: 784 - # bulk insert chunk size - bulk_insert_chunk_size: 10 - # distance_type, which should be "l1", "l2" "angle", "hamming", "cosine", "normalizedangle", "normalizedcosine" or "jaccard" - distance_type: l2 - # object_type, which should be "float" or "uint8" - object_type: float - # creation edge size - creation_edge_size: 20 - # search edge size - search_edge_size: 10 - # The limit duration of automatic indexing - # auto_index_duration_limit should be 30m-6h for production use. Below setting is a just example - auto_index_duration_limit: 1m - # Check duration of automatic indexing. - # auto_index_check_duration be 10m-1h for production use. Below setting is a just example - auto_index_check_duration: 10s - # The number of cache to trigger automatic indexing - auto_index_length: 100 - # The limit duration of auto saving indexing - # auto_save_index_duration should be 30m-60m for production use. The below setting is a just example. - auto_save_index_duration: 90s - # The maximum limit duration for an initial delay - # initial_delay_max_duration should be 3m-5m for production use. The below setting is a just example. - initial_delay_max_duration: 60s - # The default create index batch pool size. - # When it is too large comparing to machine resource, the docker container will be crash. - default_pool_size: 500 - EOF - ``` + The configuration of Vald agent for docker is set using `config.yaml`
+ You can also check [the sample configuration](https://github.com/vdaas/vald/blob/main/cmd/agent/core/ngt/sample.yaml). + + ```bash + cat << EOF > config.yaml + --- + version: v0.0.0 + time_zone: JST + logging: + logger: glg + level: debug + format: raw + server_config: + servers: + - name: agent-grpc + host: 0.0.0.0 + port: 8081 + mode: GRPC + probe_wait_time: "3s" + http: + shutdown_duration: "5s" + handler_timeout: "" + idle_timeout: "" + read_header_timeout: "" + read_timeout: "" + write_timeout: "" + startup_strategy: + - agent-grpc + shutdown_strategy: + - agent-grpc + full_shutdown_duration: 600s + tls: + enabled: false + # cert: /path/to/cert + # key: /path/to/key + # ca: /path/to/ca + ngt: + # path to index data + index_path: "/etc/server/backup" + # vector dimension + dimension: 784 + # bulk insert chunk size + bulk_insert_chunk_size: 10 + # distance_type, which should be "l1", "l2" "angle", "hamming", "cosine", "normalizedangle", "normalizedcosine" or "jaccard" + distance_type: l2 + # object_type, which should be "float" or "uint8" + object_type: float + # creation edge size + creation_edge_size: 20 + # search edge size + search_edge_size: 10 + # The limit duration of automatic indexing + # auto_index_duration_limit should be 30m-6h for production use. Below setting is a just example + auto_index_duration_limit: 1m + # Check duration of automatic indexing. + # auto_index_check_duration be 10m-1h for production use. Below setting is a just example + auto_index_check_duration: 10s + # The number of cache to trigger automatic indexing + auto_index_length: 100 + # The limit duration of auto saving indexing + # auto_save_index_duration should be 30m-60m for production use. The below setting is a just example. + auto_save_index_duration: 90s + # The maximum limit duration for an initial delay + # initial_delay_max_duration should be 3m-5m for production use. The below setting is a just example. + initial_delay_max_duration: 60s + # The default create index batch pool size. + # When it is too large comparing to machine resource, the docker container will be crash. + default_pool_size: 500 + EOF + ``` 1. Create a backup directory - To avoid removing the indexing data due to any trouble after finishing indexing, we should prepare the path for auto backup. + To avoid removing the indexing data due to any trouble after finishing indexing, we should prepare the path for auto backup. - ```bash - mkdir -p backup - ``` + ```bash + mkdir -p backup + ``` 1. Deploy Vald Agent on Docker - To deploy Vald agent on docker with `config.yaml`, you can run the below command. - Note: - - Please check whether there is `config.yaml` file and `backup` directory in your current directory. + To deploy Vald agent on docker with `config.yaml`, you can run the below command. + Note: + + - Please check whether there is `config.yaml` file and `backup` directory in your current directory. - ```bash - docker run -v $(pwd):/etc/server -u "$(id -u $USER):$(id -g $USER)" -v /etc/passwd:/etc/passwd:ro -v /etc/group:/etc/group:ro -p 8081:8081 --rm -it vdaas/vald-agent-ngt - ``` + ```bash + docker run -v $(pwd):/etc/server -u "$(id -u $USER):$(id -g $USER)" -v /etc/passwd:/etc/passwd:ro -v /etc/group:/etc/group:ro -p 8081:8081 --rm -it vdaas/vald-agent-ngt + ``` 1. Verify - If the deployment success, you can confirm the output will be similar to below. + If the deployment success, you can confirm the output will be similar to below. - ```bash - 2020-07-01 03:02:41 [INFO]: maxprocs: Leaving GOMAXPROCS=4: CPU quota undefined - 2020-07-01 03:02:41 [INFO]: service agent ngt v0.0.0 starting... - 2020-07-01 03:02:41 [INFO]: executing daemon pre-start function - 2020-07-01 03:02:41 [INFO]: executing daemon start function - 2020-07-01 03:02:41 [INFO]: server agent-grpc executing preStartFunc - 2020-07-01 12:02:41 [INFO]: gRPC server agent-grpc starting on 0.0.0.0:8081 - ``` + ```bash + 2020-07-01 03:02:41 [INFO]: maxprocs: Leaving GOMAXPROCS=4: CPU quota undefined + 2020-07-01 03:02:41 [INFO]: service agent ngt v0.0.0 starting... + 2020-07-01 03:02:41 [INFO]: executing daemon pre-start function + 2020-07-01 03:02:41 [INFO]: executing daemon start function + 2020-07-01 03:02:41 [INFO]: server agent-grpc executing preStartFunc + 2020-07-01 12:02:41 [INFO]: gRPC server agent-grpc starting on 0.0.0.0:8081 + ``` ## Run using example code 1. Download dataset - This tutorial uses [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) as a dataset for indexing and search query. + This tutorial uses [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) as a dataset for indexing and search query. - ```bash - # move to the working directory - cd example/client/agent - ``` + ```bash + # move to the working directory + cd example/client/agent + ``` - ```bash - # download Fashion-MNIST testing dataset - wget http://ann-benchmarks.com/fashion-mnist-784-euclidean.hdf5 - ``` + ```bash + # download Fashion-MNIST testing dataset + wget http://ann-benchmarks.com/fashion-mnist-784-euclidean.hdf5 + ``` 1. Running example - Vald provides multiple language client libraries such as Go, Java, Node.js, Python, etc.
- This example will insert and index 400 vectors into the Vald from the Fashion-MNIST dataset via gRPC. - And then, after waiting for indexing, it will request to search the nearest vector 10 times. - You will get the 10 nearest neighbor vectors for each search query.
+ Vald provides multiple language client libraries such as Go, Java, Node.js, Python, etc.
+ This example will insert and index 400 vectors into the Vald from the Fashion-MNIST dataset via gRPC. + And then, after waiting for indexing, it will request to search the nearest vector 10 times. + You will get the 10 nearest neighbor vectors for each search query.
- We use [`example/client/agent/main.go`](https://github.com/vdaas/vald/blob/main/example/client/agent/main.go) to run the example. - The example code is the same as running an example-only Vald agent on Kubernetes. - If you want to learn the detail of running an example, please refer to the tutorial of [Vald Agent Standalone on kubernetes](../tutorial/get-started.md/#run-using-example-code). + We use [`example/client/agent/main.go`](https://github.com/vdaas/vald/blob/main/example/client/agent/main.go) to run the example. + The example code is the same as running an example-only Vald agent on Kubernetes. + If you want to learn the detail of running an example, please refer to the tutorial of [Vald Agent Standalone on kubernetes](../tutorial/get-started.md/#run-using-example-code). - ```bash - # run example - go run main.go - ``` + ```bash + # run example + go run main.go + ``` -
- It would be best to run CreateIndex() after Insert() without waiting for auto-indexing in your client code, even if you can wait for the finishing auto createIndex function, which sometimes takes a long time.
- The backup files (e.g., ngt-meta.kvsdb) will be in your mount directory when vald-agent-ngt finishes indexing.
-
+
+ It would be best to run CreateIndex() after Insert() without waiting for auto-indexing in your client code, even if you can wait for the finishing auto createIndex function, which sometimes takes a long time.
+ The backup files (e.g., ngt-meta.kvsdb) will be in your mount directory when vald-agent-ngt finishes indexing.
+
-
- If you use Go(v1.16~) and catch the error like `missing go.sum entry to add it` when running `go run main.go`, please run `go mod tidy` and retry.
- This error comes from Go Command Changes of Go 1.16 Release Notes.(Please refer to https://golang.org/doc/go1.16#go-command for more details).
-
+
+ If you use Go(v1.16~) and catch the error like `missing go.sum entry to add it` when running `go run main.go`, please run `go mod tidy` and retry.
+ This error comes from Go Command Changes of Go 1.16 Release Notes.(Please refer to https://golang.org/doc/go1.16#go-command for more details).
+
1. Clean Up
- Stop the Vald Agent docker container via `Ctrl+C`.
+ Stop the Vald Agent docker container via `Ctrl+C`.
## Recommended Documents
Congratulation! You achieved this tutorial!
If you want, you can try other tutorials such as:
+
- [Vald Agent Standalone on Kubernetes](../tutorial/vald-agent-standalone-on-k8s.md)
For more information, we recommend you to check:
+
- [Architecture](../overview/architecture.md)
- [Configuration](../user-guides/configuration.md)
- [Operations](../user-guides/operations.md)
diff --git a/docs/tutorial/vald-agent-standalone-on-k8s.md b/docs/tutorial/vald-agent-standalone-on-k8s.md
index 530479aad6..bdfcec848f 100644
--- a/docs/tutorial/vald-agent-standalone-on-k8s.md
+++ b/docs/tutorial/vald-agent-standalone-on-k8s.md
@@ -16,6 +16,7 @@ Using only Vald Agent, the auto indexing function is not in use.
- Vald will run on Cloud Services such as GKE, AWS. - In the sense of trying to "Get-Started", [k3d](https://k3d.io/) or [kind](https://kind.sigs.k8s.io/) are easy Kubernetes tools to use. + To complete get started, the Kubernetes cluster is required.
+ Vald will run on Cloud Services such as GKE, AWS. + In the sense of trying to "Get-Started", [k3d](https://k3d.io/) or [kind](https://kind.sigs.k8s.io/) are easy Kubernetes tools to use. ## Deploy Vald Agent Standalone on Kubernetes Cluster @@ -70,46 +71,46 @@ This chapter uses [NGT](https://github.com/yahoojapan/ngt) as Vald Agent to perf 1. Clone the repository - To use the `deployment yaml` for deployment, let's clone [`vdaas/vald`](https://github.com/vdaas/vald.git) repository. + To use the `deployment yaml` for deployment, let's clone [`vdaas/vald`](https://github.com/vdaas/vald.git) repository. - ```bash - git clone https://github.com/vdaas/vald.git && \ - cd vald - ``` + ```bash + git clone https://github.com/vdaas/vald.git && \ + cd vald + ``` 1. Deploy Vald Agent Standalone using Helm - There is the [values.yaml](https://github.com/vdaas/vald/blob/main/example/helm/values-standalone-agent-ngt.yaml) to deploy standalone Vald Agent. - Each component can be disabled by setting the value `false` to the `[component].enabled` field. - This is useful for deploying standalone Vald Agent NGT pods. + There is the [values.yaml](https://github.com/vdaas/vald/blob/main/example/helm/values-standalone-agent-ngt.yaml) to deploy standalone Vald Agent. + Each component can be disabled by setting the value `false` to the `[component].enabled` field. + This is useful for deploying standalone Vald Agent NGT pods. - ```bash - helm repo add vald https://vald.vdaas.org/charts && \ - helm install vald-agent-ngt vald/vald --values example/helm/values-standalone-agent-ngt.yaml - ``` + ```bash + helm repo add vald https://vald.vdaas.org/charts && \ + helm install vald-agent-ngt vald/vald --values example/helm/values-standalone-agent-ngt.yaml + ``` 1. Verify - ```bash - kubectl get pods - ``` + ```bash + kubectl get pods + ``` -
Example output
- If the deployment is successful, Vald Agent component should be running. +
Example output
+ If the deployment is successful, Vald Agent component should be running. - ```bash - NAME READY STATUS RESTARTS AGE - vald-agent-ngt-0 1/1 Running 0 20m - vald-agent-ngt-1 1/1 Running 0 20m - vald-agent-ngt-2 1/1 Running 0 20m - vald-agent-ngt-3 1/1 Running 0 20m - ``` + ```bash + NAME READY STATUS RESTARTS AGE + vald-agent-ngt-0 1/1 Running 0 20m + vald-agent-ngt-1 1/1 Running 0 20m + vald-agent-ngt-2 1/1 Running 0 20m + vald-agent-ngt-3 1/1 Running 0 20m + ``` -
This example will insert and index 400 vectors into the Vald from the Fashion-MNIST dataset via gRPC. @@ -147,89 +148,89 @@ This chapter uses [NGT](https://github.com/yahoojapan/ngt) as Vald Agent to perf
The detailed explanation of example code is here
This will execute 6 steps. - 1. init + 1. init - Import packages
example code
- ```go - package main - - import ( - "context" - "encoding/json" - "flag" - "time" - - "github.com/kpango/fuid" - "github.com/kpango/glg" - agent "github.com/vdaas/vald-client-go/v1/agent/core" - "github.com/vdaas/vald-client-go/v1/vald" - "github.com/vdaas/vald-client-go/v1/payload" - - "gonum.org/v1/hdf5" - "google.golang.org/grpc" - ) - ``` + ```go + package main + + import ( + "context" + "encoding/json" + "flag" + "time" + + "github.com/kpango/fuid" + "github.com/kpango/glg" + agent "github.com/vdaas/vald-client-go/v1/agent/core" + "github.com/vdaas/vald-client-go/v1/vald" + "github.com/vdaas/vald-client-go/v1/payload" + + "gonum.org/v1/hdf5" + "google.golang.org/grpc" + ) + ```
example code
+ - The constant number of training datasets and test datasets. +
example code
- ```go - const ( - insertCount = 400 - testCount = 20 - ) - ``` + ```go + const ( + insertCount = 400 + testCount = 20 + ) + ``` -
example code
+ - The variables for configuration. +
example code
- ```go - const ( - datasetPath string - grpcServerAddr string - indexingWaitSeconds uint - ) - ``` + ```go + const ( + datasetPath string + grpcServerAddr string + indexingWaitSeconds uint + ) + ``` -
example code
- ```go - func init() { - flag.StringVar(&datasetPath, "path", "fashion-mnist-784-euclidean.hdf5", "set dataset path") - flag.StringVar(&grpcServerAddr, "addr", "127.0.0.1:8081", "set gRPC server address") - flag.UintVar(&indexingWaitSeconds, "wait", 60, "set indexing wait seconds") - flag.Parse() - } - ``` + ```go + func init() { + flag.StringVar(&datasetPath, "path", "fashion-mnist-784-euclidean.hdf5", "set dataset path") + flag.StringVar(&grpcServerAddr, "addr", "127.0.0.1:8081", "set gRPC server address") + flag.UintVar(&indexingWaitSeconds, "wait", 60, "set indexing wait seconds") + flag.Parse() + } + ```
example code
- ```go - ids, train, test, err := load(datasetPath) - if err != nil { - glg.Fatal(err) - } - ``` + ```go + ids, train, test, err := load(datasetPath) + if err != nil { + glg.Fatal(err) + } + ```
example code
```go @@ -245,129 +246,130 @@ This chapter uses [NGT](https://github.com/yahoojapan/ngt) as Vald Agent to perf
example code
- ```go - for i := range ids [:insertCount] { - if i%10 == 0 { - glg.Infof("Inserted %d", i) - } - _, err := client.Insert(ctx, &payload.Insert_Request{ - Vector: &payload.Object_Vector{ - Id: ids[i], - Vector: train[i], - }, - Config: &payload.Insert_Config{ - SkipStrictExistCheck: true, - }, - }) - if err != nil { - glg.Fatal(err) - } - } - ``` + ```go + for i := range ids [:insertCount] { + if i%10 == 0 { + glg.Infof("Inserted %d", i) + } + _, err := client.Insert(ctx, &payload.Insert_Request{ + Vector: &payload.Object_Vector{ + Id: ids[i], + Vector: train[i], + }, + Config: &payload.Insert_Config{ + SkipStrictExistCheck: true, + }, + }) + if err != nil { + glg.Fatal(err) + } + } + ```
example code
- ```go - wt := time.Duration(indexingWaitSeconds) * time.Second - glg.Infof("Wait %s for indexing to finish", wt) - time.Sleep(wt) - ``` + ```go + wt := time.Duration(indexingWaitSeconds) * time.Second + glg.Infof("Wait %s for indexing to finish", wt) + time.Sleep(wt) + ```
example code
+ You can set Agent NGT configuration `auto_index_duration_limit` and `auto_index_check_duration` for auto indexing. + In this example, you can create index manually using `CreateAndSaveIndex()` method in the client library +
example code
- ```go - _, err = client.CreateAndSaveIndex(ctx, &payload.Control_CreateIndexRequest{ - PoolSize: uint32(insertCount), - }) - if err != nil { - glg.Fatal(err) - } - ``` + ```go + _, err = client.CreateAndSaveIndex(ctx, &payload.Control_CreateIndexRequest{ + PoolSize: uint32(insertCount), + }) + if err != nil { + glg.Fatal(err) + } + ``` -
example code
- ```go - glg.Infof("Start search %d times", testCount) - for i, vec := range test[:testCount] { - res, err := client.Search(ctx, &payload.Search_Request){ - Vector: vec, - Config: &payload.Search_Config{ - Num: 10, - Radius: -1, - Epsilon: 0.1, - Timeout: 100000000, - } - } - if err != nil { - glg.Fatal(err) - } - - b, _ := json.MarshalIndent(res.GetResults(), "", " ") - glg.Infof("%d - Results : %s\n\n", i+1, string(b)) - time.Sleep(1 * time.Second) - } - ``` + ```go + glg.Infof("Start search %d times", testCount) + for i, vec := range test[:testCount] { + res, err := client.Search(ctx, &payload.Search_Request){ + Vector: vec, + Config: &payload.Search_Config{ + Num: 10, + Radius: -1, + Epsilon: 0.1, + Timeout: 100000000, + } + } + if err != nil { + glg.Fatal(err) + } + + b, _ := json.MarshalIndent(res.GetResults(), "", " ") + glg.Infof("%d - Results : %s\n\n", i+1, string(b)) + time.Sleep(1 * time.Second) + } + ```
example code
+ - Remove indexed 400 training datasets from the Vald agent. +
example code
- ```go - for i := range ids [:insertCount] { - _, err := client.Remove(ctx, &payload.Remove_Request{ - Id: &payload.Object_ID{ - Id: ids[i], - }, - }) - if err != nil { - glg.Fatal(err) - } - if i%10 == 0 { - glg.Infof("Removed %d", i) + ```go + for i := range ids [:insertCount] { + _, err := client.Remove(ctx, &payload.Remove_Request{ + Id: &payload.Object_ID{ + Id: ids[i], + }, + }) + if err != nil { + glg.Fatal(err) + } + if i%10 == 0 { + glg.Infof("Removed %d", i) + } } - } - ``` + ``` -
example code
+ - Remove from the index manually instead of waiting for auto indexing. + The removed vectors still exist in the NGT graph index before the SaveIndex (or CreateAndSaveIndex) API is called. + If you run the below code, the indexes will be removed completely from the Vald Agent NGT graph and the Backup file. +
example code
- ```go - _, err = client.SaveIndex(ctx, &payload.Empty{}) - if err != nil { - glg.Fatal(err) - } - ``` + ```go + _, err = client.SaveIndex(ctx, &payload.Empty{}) + if err != nil { + glg.Fatal(err) + } + ``` -
It would be best to run CreateIndex() after Insert() without waiting for auto-indexing in your client code, even you can wait for the finishing auto createIndex function, which sometimes takes a long time.
@@ -392,6 +394,7 @@ helm uninstall vald-agent-ngt
Congratulation! You achieved this tutorial!
For more information, we recommend you to check:
+
- [Architecture](../overview/architecture.md)
- [Configuration](../user-guides/configuration.md)
- [Operations](../user-guides/operations.md)
diff --git a/docs/usecase/usage-example.md b/docs/usecase/usage-example.md
index 84adca2a25..1e77873727 100644
--- a/docs/usecase/usage-example.md
+++ b/docs/usecase/usage-example.md
@@ -1,23 +1,23 @@
# Use cases
- This document will introduce you to the example of what Vald can do.
- Vald is a highly scalable distributed fast approximate nearest neighbor dense vector search engine, which uses [NGT](https://github.com/yahoojapan/NGT) as the core engine of Vald, and Vald manages to integrate with Kubernetes.
+This document will introduce you to the example of what Vald can do.
+Vald is a highly scalable distributed fast approximate nearest neighbor dense vector search engine, which uses [NGT](https://github.com/yahoojapan/NGT) as the core engine of Vald, and Vald manages to integrate with Kubernetes.
- You cannot generally search your unstructured data using the inverted index, like images and videos.
- Applying a model like BERT or VGG can convert your unstructured data into vectors.
- After converting them into vectors, you can insert them into the Vald cluster and process them in the Vald cluster.
+You cannot generally search your unstructured data using the inverted index, like images and videos.
+Applying a model like BERT or VGG can convert your unstructured data into vectors.
+After converting them into vectors, you can insert them into the Vald cluster and process them in the Vald cluster.
- Here are some general use cases of Vald or vector search engines.
+Here are some general use cases of Vald or vector search engines.
## Image and video processing
- You can use Vald as the image/video processing engine to search the similar images/videos or analyze the image/video for your use case.
+You can use Vald as the image/video processing engine to search the similar images/videos or analyze the image/video for your use case.
- Vald is capable of processing a huge number of images at the same time, so its case fits with your use case.
+Vald is capable of processing a huge number of images at the same time, so its case fits with your use case.
- Here are some examples of what you can do with images and videos using Vald.
+Here are some examples of what you can do with images and videos using Vald.
- Search by image
- Face recognition
@@ -29,11 +29,11 @@
- Audio processing is important for personal assistant implementation.
+Audio processing is important for personal assistant implementation.
- Vald can act as a brain of the personal assistant function, conversation interpreter, and natural language generation.
+Vald can act as a brain of the personal assistant function, conversation interpreter, and natural language generation.
- Here are some examples of what you can process using Vald.
+Here are some examples of what you can process using Vald.
- Personal assistant
- Speech recognition
@@ -43,9 +43,9 @@
- Using a text vectorizing model like BERT, you can process your text data in Vald.
+Using a text vectorizing model like BERT, you can process your text data in Vald.
- Here are some examples of the use case of text processing using Vald.
+Here are some examples of the use case of text processing using Vald.
- Search by text
- Product recommendation based on text
@@ -56,9 +56,9 @@
- Vald can process the vector data, you can analyze every data you can vectorize.
+Vald can process the vector data, you can analyze every data you can vectorize.
- Here are some examples of the use case of data analysis.
+Here are some examples of the use case of data analysis.
- AI malware detection
@@ -68,7 +68,7 @@
- Price optimization
- By applying the price optimization technique using Vald, you can find the most optimized price for your business.
+ By applying the price optimization technique using Vald, you can find the most optimized price for your business.
You can apply models like GLMs to achieve it and use Vald as a machine learning engine for your business.
- Social analysis
@@ -78,10 +78,10 @@
## Advanced use cases
- Besides the general use case of Vald or vector search engine, Vald supports a user-defined filter that the user can customize the filter to filter the specific result.
+Besides the general use case of Vald or vector search engine, Vald supports a user-defined filter that the user can customize the filter to filter the specific result.
- For example when the user chose a man's t-shirt and the recommended product is going to be searched in Vald.
+For example when the user chose a man's t-shirt and the recommended product is going to be searched in Vald.
- Without the filtering functionality, the women's t-shirt may be searched in Vald and displayed because women's t-shirt is similar to the men's t-shirt and it is very hard to differentiate the image of men's and women's t-shirt.
+Without the filtering functionality, the women's t-shirt may be searched in Vald and displayed because women's t-shirt is similar to the men's t-shirt and it is very hard to differentiate the image of men's and women's t-shirt.
- By implementing the custom filter, you can filter only the man's t-shirt based on your criteria and needs.
+By implementing the custom filter, you can filter only the man's t-shirt based on your criteria and needs.
diff --git a/docs/user-guides/client-api-config.md b/docs/user-guides/client-api-config.md
index e9a1822d9f..a42c858a28 100644
--- a/docs/user-guides/client-api-config.md
+++ b/docs/user-guides/client-api-config.md
@@ -371,7 +371,6 @@ message Filter {
`timestamp` is the timestamp when the vector is inserted or updated.
When `timestamp` is not set, the current time will be used.
-
## Search Service
Vald provides four types of search services.
diff --git a/docs/user-guides/cluster-role-binding.md b/docs/user-guides/cluster-role-binding.md
index d3bd0d19e2..3ade19d6d6 100644
--- a/docs/user-guides/cluster-role-binding.md
+++ b/docs/user-guides/cluster-role-binding.md
@@ -38,7 +38,7 @@ apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: discoverer
-...
+---
rules:
- apiGroups:
- apps
@@ -109,23 +109,22 @@ To customize the cluster role configuration on the Helm chart for Vald Discovere
```yaml
discoverer:
-...
- clusterRole:
- # discoverer.clusterRole.enabled -- creates clusterRole resource
- enabled: true
- # discoverer.clusterRole.name -- name of clusterRole
- name: discoverer
- clusterRoleBinding:
- # discoverer.clusterRoleBinding.enabled -- creates clusterRoleBinding resource
- enabled: true
- # discoverer.clusterRoleBinding.name -- name of clusterRoleBinding
- name: discoverer
- serviceAccount:
- # discoverer.serviceAccount.enabled -- creates service account
- enabled: true
- # discoverer.serviceAccount.name -- name of service account
- name: vald
-...
+---
+clusterRole:
+ # discoverer.clusterRole.enabled -- creates clusterRole resource
+ enabled: true
+ # discoverer.clusterRole.name -- name of clusterRole
+ name: discoverer
+clusterRoleBinding:
+ # discoverer.clusterRoleBinding.enabled -- creates clusterRoleBinding resource
+ enabled: true
+ # discoverer.clusterRoleBinding.name -- name of clusterRoleBinding
+ name: discoverer
+serviceAccount:
+ # discoverer.serviceAccount.enabled -- creates service account
+ enabled: true
+ # discoverer.serviceAccount.name -- name of service account
+ name: vald
```
- You can use Vald as the image/video processing engine to search the similar images/videos or analyze the image/video for your use case.
+You can use Vald as the image/video processing engine to search the similar images/videos or analyze the image/video for your use case.
- Vald is capable of processing a huge number of images at the same time, so its case fits with your use case.
+Vald is capable of processing a huge number of images at the same time, so its case fits with your use case.
- Here are some examples of what you can do with images and videos using Vald.
+Here are some examples of what you can do with images and videos using Vald.
- Search by image
- Face recognition
@@ -29,11 +29,11 @@
- Audio processing is important for personal assistant implementation.
+Audio processing is important for personal assistant implementation.
- Vald can act as a brain of the personal assistant function, conversation interpreter, and natural language generation.
+Vald can act as a brain of the personal assistant function, conversation interpreter, and natural language generation.
- Here are some examples of what you can process using Vald.
+Here are some examples of what you can process using Vald.
- Personal assistant
- Speech recognition
@@ -43,9 +43,9 @@
- Using a text vectorizing model like BERT, you can process your text data in Vald.
+Using a text vectorizing model like BERT, you can process your text data in Vald.
- Here are some examples of the use case of text processing using Vald.
+Here are some examples of the use case of text processing using Vald.
- Search by text
- Product recommendation based on text
@@ -56,9 +56,9 @@
- Vald can process the vector data, you can analyze every data you can vectorize.
+Vald can process the vector data, you can analyze every data you can vectorize.
- Here are some examples of the use case of data analysis.
+Here are some examples of the use case of data analysis.
- AI malware detection
@@ -68,7 +68,7 @@
- Price optimization
- By applying the price optimization technique using Vald, you can find the most optimized price for your business.
+ By applying the price optimization technique using Vald, you can find the most optimized price for your business.
You can apply models like GLMs to achieve it and use Vald as a machine learning engine for your business.
- Social analysis
@@ -78,10 +78,10 @@
## Advanced use cases
- Besides the general use case of Vald or vector search engine, Vald supports a user-defined filter that the user can customize the filter to filter the specific result.
+Besides the general use case of Vald or vector search engine, Vald supports a user-defined filter that the user can customize the filter to filter the specific result.
- For example when the user chose a man's t-shirt and the recommended product is going to be searched in Vald.
+For example when the user chose a man's t-shirt and the recommended product is going to be searched in Vald.
- Without the filtering functionality, the women's t-shirt may be searched in Vald and displayed because women's t-shirt is similar to the men's t-shirt and it is very hard to differentiate the image of men's and women's t-shirt.
+Without the filtering functionality, the women's t-shirt may be searched in Vald and displayed because women's t-shirt is similar to the men's t-shirt and it is very hard to differentiate the image of men's and women's t-shirt.
- By implementing the custom filter, you can filter only the man's t-shirt based on your criteria and needs.
+By implementing the custom filter, you can filter only the man's t-shirt based on your criteria and needs.
diff --git a/docs/user-guides/client-api-config.md b/docs/user-guides/client-api-config.md
index e9a1822d9f..a42c858a28 100644
--- a/docs/user-guides/client-api-config.md
+++ b/docs/user-guides/client-api-config.md
@@ -371,7 +371,6 @@ message Filter {
`timestamp` is the timestamp when the vector is inserted or updated.
When `timestamp` is not set, the current time will be used.
-
## Search Service
Vald provides four types of search services.
diff --git a/docs/user-guides/cluster-role-binding.md b/docs/user-guides/cluster-role-binding.md
index d3bd0d19e2..3ade19d6d6 100644
--- a/docs/user-guides/cluster-role-binding.md
+++ b/docs/user-guides/cluster-role-binding.md
@@ -38,7 +38,7 @@ apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: discoverer
-...
+---
rules:
- apiGroups:
- apps
@@ -109,23 +109,22 @@ To customize the cluster role configuration on the Helm chart for Vald Discovere
```yaml
discoverer:
-...
- clusterRole:
- # discoverer.clusterRole.enabled -- creates clusterRole resource
- enabled: true
- # discoverer.clusterRole.name -- name of clusterRole
- name: discoverer
- clusterRoleBinding:
- # discoverer.clusterRoleBinding.enabled -- creates clusterRoleBinding resource
- enabled: true
- # discoverer.clusterRoleBinding.name -- name of clusterRoleBinding
- name: discoverer
- serviceAccount:
- # discoverer.serviceAccount.enabled -- creates service account
- enabled: true
- # discoverer.serviceAccount.name -- name of service account
- name: vald
-...
+---
+clusterRole:
+ # discoverer.clusterRole.enabled -- creates clusterRole resource
+ enabled: true
+ # discoverer.clusterRole.name -- name of clusterRole
+ name: discoverer
+clusterRoleBinding:
+ # discoverer.clusterRoleBinding.enabled -- creates clusterRoleBinding resource
+ enabled: true
+ # discoverer.clusterRoleBinding.name -- name of clusterRoleBinding
+ name: discoverer
+serviceAccount:
+ # discoverer.serviceAccount.enabled -- creates service account
+ enabled: true
+ # discoverer.serviceAccount.name -- name of service account
+ name: vald
```
@@ -139,6 +138,7 @@ If you want to modify or disable these configurations, you need to grant the [cl
Please refer to the official guidelines to configure cluster role configuration for your cloud provider, and configure the service account name for Vald Discoverer.
For example:
+
- [Amazon EKS](https://docs.aws.amazon.com/eks/latest/userguide/add-user-role.html)
- [GKE](https://cloud.google.com/kubernetes-engine/docs/how-to/role-based-access-control)
diff --git a/docs/user-guides/configuration.md b/docs/user-guides/configuration.md
index c2671f8ab4..b3b9e06d88 100644
--- a/docs/user-guides/configuration.md
+++ b/docs/user-guides/configuration.md
@@ -308,7 +308,6 @@ Each configuration file is the following:
- [Cluster role binding](https://github.com/vdaas/vald/blob/main/k8s/discoverer/clusterrolebinding.yaml)
- [Service account](https://github.com/vdaas/vald/blob/main/k8s/discoverer/serviceaccount.yaml)
-
#### Resource requests and limits
The number of discoverer pods and resource limits can be estimated by the configurations of your LB gateways and index managers because its APIs are called by them.
@@ -361,7 +360,6 @@ For further details, there are references to the Helm values in the Vald GitHub
-
[vald-helm-chart]: https://github.com/vdaas/vald/tree/main/charts/vald
[vald-helm-operator-chart]: https://github.com/vdaas/vald/tree/main/charts/vald-helm-operator
[vald-apis-docs]: https://github.com/vdaas/vald/tree/main/apis/docs
diff --git a/docs/user-guides/deployment.md b/docs/user-guides/deployment.md
index 9279317de6..9ab1faaa80 100644
--- a/docs/user-guides/deployment.md
+++ b/docs/user-guides/deployment.md
@@ -3,15 +3,15 @@
There are two major ways for the deployment of the Vald cluster ways: Using the Helm command with `values.yaml` or without Helm command using operator called `vald-helm-operator`.
- Using Helm command with `values.yaml`
- - Easy to deploy
- - Allow editing Vald configuration values when the user executes Helm command with inlining.
- - Need Helm command when applying configuration
-- Using `vald-helm-operator`
- - Monitoring the Vald deployments and Vald custom resource by `vald-helm-operator`
- - Use `kubectl` command to set or update of the Vald cluster configuration
- - Automate manage the Vald cluster based on CRD (Custom Resource Definitions)
+ - Easy to deploy
+ - Allow editing Vald configuration values when the user executes Helm command with inlining.
+ - Need Helm command when applying configuration
+- Using `vald-helm-operator`
+ - Monitoring the Vald deployments and Vald custom resource by `vald-helm-operator`
+ - Use `kubectl` command to set or update of the Vald cluster configuration
+ - Automate manage the Vald cluster based on CRD (Custom Resource Definitions)
The benefit of using vald-helm-operator, it eliminates the need to check the status of the Vald cluster from the Vald cluster operator.
@@ -39,6 +39,7 @@ Both ways need the configuration file when you need to overwrite the default con
Please refer to [the basic configuration document](../user-guides/configuration.md).
In addition, as you need, please refer to the followings:
+
- [Backup Configuration](../user-guides/backup-configuration.md)
- [Filter Configuration](../user-guides/filtering-configuration.md)
@@ -118,15 +119,15 @@ After create `values.yaml`, you can deploy by the following steps.
1. Add vald repo into the helm repo.
- ```bash
- helm repo add vald https://vdaas.vald.org/charts
- ```
+ ```bash
+ helm repo add vald https://vdaas.vald.org/charts
+ ```
1. Deploy with the `values.yaml`
- ```bash
- helm install vald vald/vald --values
- ```
+ ```bash
+ helm install vald vald/vald --values
+ ```
### Update Configuration
@@ -185,7 +186,7 @@ spec:
minReplicas: 2
maxReplicas: 2
gateway_config:
- index_replica: 2
+ index_replica: 2
## vald-agent settings
agent:
@@ -243,7 +244,6 @@ spec:
For more details of the configuration of vald-helm-operator-release, please refer to [here](https://github.com/vdaas/vald/tree/main/charts/vald-helm-operator#configuration).
-
After setting `vr.yaml` (and `vhor.yaml`), you can deploy by the following steps.
@@ -252,29 +252,29 @@ If you need to deploy ValdHelmOperatorRelease, you should apply vhor.yaml before
1. Add vald repo into the helm repo
- ```bash
- helm repo add vald https://vdaas.vald.org
- ```
+ ```bash
+ helm repo add vald https://vdaas.vald.org
+ ```
1. Deploy `vald-helm-operator`
- ```bash
- helm install vald-helm-operator-release vald/vald-helm-operator
- ```
+ ```bash
+ helm install vald-helm-operator-release vald/vald-helm-operator
+ ```
1. Apply `vhor.yaml` (optional)
- ```bash
- kubectl apply -f vhor.yaml
- ```
+ ```bash
+ kubectl apply -f vhor.yaml
+ ```
- After deployment success, the Kubernetes cluster runs the rolling update for vald-helm-operator components.
+ After deployment success, the Kubernetes cluster runs the rolling update for vald-helm-operator components.
1. Apply `vr.yaml`
- ```bash
- kubectl apply -f vr.yaml
- ```
+ ```bash
+ kubectl apply -f vr.yaml
+ ```
### Update Configuration
@@ -290,23 +290,22 @@ The Vald cluster can be removed by the following steps.
1. Delete the `ValdRelease`
- ```bash
- kubectl delete -f vr.yaml
- ```
+ ```bash
+ kubectl delete -f vr.yaml
+ ```
1. Delete the `ValdHelmOperatorReleases`
- ```bash
- kubectl delete -f vhor.yaml
- ```
+ ```bash
+ kubectl delete -f vhor.yaml
+ ```
1. Delete crd
- ```bash
- kubectl patch crd/valdhelmopratorreleases.vald.vdaas.org -p '{"metadata":{"finalizers":[]}}' -type=merge
- ```
+ ```bash
+ kubectl patch crd/valdhelmopratorreleases.vald.vdaas.org -p '{"metadata":{"finalizers":[]}}' -type=merge
+ ```
-
## Upgrading
Our versioning strategy is based on [Semantic Versioning][semver].
Upgrading to a new version, such as minor or major, may require changing your configurations.
-Please read the [CHANGELOG][CHANGELOG] before upgrading.
+Please read the [CHANGELOG][changelog] before upgrading.
### In case of manual deploy
@@ -129,23 +128,17 @@ On the other hand, please update the operator's deployment manually.
After that, please update `image.tag` field in your valdrelease (or `vr`) resource.
The operator will automatically detect the changes and update the deployed Vald cluster.
-
-
-
### References
- [vald-helm-chart][vald-helm-chart]
- [vald-helm-operator-chart][vald-helm-operator-chart]
-- [CHANGELOG][CHANGELOG]
-
+- [CHANGELOG][changelog]
[vald-helm-chart]: https://github.com/vdaas/vald/tree/main/charts/vald
[vald-helm-operator-chart]: https://github.com/vdaas/vald/tree/main/charts/vald-helm-operator
-[CHANGELOG]: https://vald.vdaas.org/docs/release/changelog/
-
+[changelog]: https://vald.vdaas.org/docs/release/changelog/
[pod-priority-preemption]: https://kubernetes.io/docs/concepts/configuration/pod-priority-preemption/
[resource-quota]: https://kubernetes.io/docs/concepts/policy/resource-quotas/
-
[prometheus-io]: https://prometheus.io/
[grafana]: https://grafana.com
[vald-prometheus]: https://github.com/vdaas/vald/tree/main/k8s/metrics/prometheus
diff --git a/hack/CHANGELOG.template.md b/hack/CHANGELOG.template.md
index 38e37a7536..0a9df0b417 100644
--- a/hack/CHANGELOG.template.md
+++ b/hack/CHANGELOG.template.md
@@ -54,6 +54,7 @@
### Documents
+
- [GoDoc](https://pkg.go.dev/github.com/vdaas/vald@{{ version }})
- [Helm Chart Reference](https://github.com/vdaas/vald/blob/{{ version }}/charts/vald/README.md)
- [Helm Operator Chart Reference](https://github.com/vdaas/vald/blob/{{ version }}/charts/vald-helm-operator/README.md)
diff --git a/hack/benchmark/internal/db/nosql/cassandra/testdata.json b/hack/benchmark/internal/db/nosql/cassandra/testdata.json
index b9cf09e865..7afdd00da9 100644
--- a/hack/benchmark/internal/db/nosql/cassandra/testdata.json
+++ b/hack/benchmark/internal/db/nosql/cassandra/testdata.json
@@ -3,488 +3,366 @@
"uuid": "8a8dfec5-2115-45ae-9a07-f8634ff61477",
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+ "ips": ["8.144.247.69"]
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+ "ips": ["251.180.215.252"]
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+ "ips": ["230.135.113.209"]
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+ "ips": ["133.129.82.227"]
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+ "ips": ["22.74.94.83"]
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- "ips": [
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+ "ips": ["57.10.1.105"]
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{
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- "ips": [
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+ "ips": ["108.59.52.87"]
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{
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- "ips": [
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+ "ips": ["178.72.12.38"]
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"vector": "Elit id qui elit nostrud sit exercitation aliquip. Minim nostrud cillum amet esse consequat nulla ex in tempor nulla sit. Sint fugiat pariatur qui aliquip. Elit mollit laboris ex officia consequat cupidatat nostrud velit amet fugiat ullamco adipisicing ad. Incididunt excepteur et aliqua id cillum consectetur anim anim duis.",
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- "ips": [
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+ "ips": ["96.211.93.91"]
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{
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- "ips": [
- "100.86.51.200"
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+ "ips": ["100.86.51.200"]
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{
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"meta": "Et veniam quis consectetur adipisicing elit. Laboris veniam aliquip quis enim dolor nostrud consectetur. Ad reprehenderit minim incididunt magna eiusmod esse proident aliqua aute. Sunt sint ex minim fugiat nostrud consequat nisi.",
- "ips": [
- "34.57.190.71"
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+ "ips": ["34.57.190.71"]
},
{
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"vector": "Dolor cillum ad sit commodo magna dolore incididunt esse ipsum dolore. Deserunt consequat pariatur aliqua occaecat fugiat non et consequat ex nisi dolore tempor. Dolor nostrud consequat ipsum tempor et sint dolore minim consectetur eu. Enim reprehenderit est aliquip sint est ipsum. Id pariatur et aliqua exercitation voluptate sunt aliqua Lorem consequat dolore. Ipsum consectetur commodo aliquip eiusmod labore occaecat proident non id Lorem cupidatat ea id minim. Exercitation aliqua nostrud adipisicing qui nostrud quis nulla proident officia eiusmod anim.",
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- "ips": [
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+ "ips": ["103.116.240.218"]
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{
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"vector": "Nulla aliqua dolore pariatur pariatur adipisicing. Dolor incididunt aliqua id nisi quis reprehenderit fugiat dolor sit. Laboris consectetur ad aliquip eu amet veniam velit esse. Eu do do consequat sit.",
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- "ips": [
- "15.63.160.66"
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+ "ips": ["15.63.160.66"]
},
{
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- "ips": [
- "219.138.44.251"
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+ "ips": ["219.138.44.251"]
},
{
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"vector": "Duis tempor ex minim ex cupidatat reprehenderit anim ipsum in do. Culpa labore nisi proident aute quis eiusmod. Irure ea ullamco aliquip ex ad amet mollit nulla. Est laborum id ea et ipsum velit est nisi officia consequat incididunt excepteur dolore. Laboris enim laborum qui mollit id consectetur sint esse duis. Proident ea qui ut occaecat amet eu pariatur consectetur deserunt esse dolor voluptate in. Amet pariatur enim non minim irure amet minim exercitation sint mollit sint adipisicing Lorem voluptate.",
"meta": "Elit magna amet do adipisicing adipisicing do velit culpa nisi excepteur magna cillum sint ex. Dolor quis Lorem irure Lorem voluptate minim ad amet officia occaecat anim ut. Nostrud eiusmod veniam cillum ea mollit ipsum mollit Lorem ut sit. Dolor aliquip aliqua incididunt nostrud voluptate qui ut nisi cupidatat velit elit anim laborum fugiat. Laboris est dolor minim est ex eiusmod reprehenderit deserunt mollit duis. Aliqua laboris amet nostrud Lorem voluptate aute reprehenderit duis velit id et consectetur non. Incididunt ex laborum labore duis sunt ut in incididunt.",
- "ips": [
- "198.98.227.210"
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+ "ips": ["198.98.227.210"]
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{
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"meta": "Culpa velit laborum eu cupidatat. Eu et ullamco enim id minim enim ullamco ex elit exercitation laboris. Culpa et anim ad incididunt eiusmod sit cillum culpa laboris commodo Lorem ut. Nostrud reprehenderit irure Lorem dolore minim deserunt ipsum id reprehenderit occaecat ipsum fugiat. Culpa eiusmod pariatur nulla quis consectetur magna irure. Enim commodo esse duis ut incididunt dolore eiusmod ipsum commodo est mollit nisi.",
- "ips": [
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+ "ips": ["90.121.224.191"]
},
{
"uuid": "0843c861-e548-475c-b4ba-5a428678b4da",
"vector": "Fugiat in irure dolore non esse culpa anim ea dolore reprehenderit voluptate elit commodo. Nisi nisi ut ut labore culpa irure incididunt elit aute quis laborum laboris duis. Sit commodo irure adipisicing sit non amet. Minim excepteur aute deserunt elit amet exercitation qui anim ea minim amet. Ad quis Lorem nulla dolore nostrud veniam cillum. Deserunt deserunt aute irure reprehenderit non deserunt ut duis officia.",
"meta": "Et dolore adipisicing amet ea. Ullamco sit duis proident dolore. Et ipsum velit do occaecat aliquip quis adipisicing duis enim in et qui. Duis incididunt nulla ad ad dolor est fugiat eiusmod non amet eiusmod. Cupidatat fugiat qui mollit magna aute ullamco veniam aliqua ea anim exercitation. Magna laborum in eu minim et excepteur velit exercitation consequat pariatur mollit aliquip. Ullamco aliqua do voluptate consequat ad laboris adipisicing anim consequat ad culpa commodo.",
- "ips": [
- "77.151.18.67"
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+ "ips": ["77.151.18.67"]
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{
"uuid": "acefb442-54cb-4f9d-ae99-6acc1efddf49",
"vector": "Amet non incididunt enim ut voluptate ea nisi est. Veniam occaecat mollit voluptate occaecat amet pariatur nisi id. In consectetur et est aute.",
"meta": "Quis est velit velit officia esse quis elit aliqua anim sint ex mollit. Velit reprehenderit do ipsum deserunt non culpa ea. Eu irure deserunt officia Lorem ullamco exercitation do sint irure mollit tempor. Ut elit eiusmod aute ut ut labore anim et officia. Tempor esse ea quis sint enim veniam dolore ad nisi laborum dolore.",
- "ips": [
- "14.70.175.205"
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+ "ips": ["14.70.175.205"]
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{
"uuid": "e929fb87-3fe9-4f67-93df-d6d84ee0f303",
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"meta": "Ullamco est duis nostrud cupidatat non irure. In est sunt amet tempor amet culpa labore id commodo sunt nulla nisi. Eiusmod amet consequat occaecat id laborum cupidatat aliquip elit. Exercitation culpa excepteur qui culpa aute dolore laboris officia qui. Occaecat ea enim id dolore labore incididunt cupidatat est quis voluptate. Id officia non aliquip sunt duis dolore magna reprehenderit incididunt duis. Cupidatat enim in irure et voluptate mollit tempor aute officia dolore proident nulla.",
- "ips": [
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+ "ips": ["216.127.244.240"]
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"vector": "Elit tempor adipisicing consectetur fugiat amet officia laborum laborum. Velit eiusmod deserunt Lorem nisi reprehenderit nulla ipsum pariatur labore eu. Occaecat cupidatat duis commodo adipisicing excepteur aliqua in reprehenderit ex amet veniam. Ipsum dolore nisi ex dolore reprehenderit incididunt aliquip esse velit deserunt reprehenderit duis commodo. Ad occaecat aliqua consectetur cillum et ipsum. Ad deserunt irure velit excepteur sunt proident cupidatat deserunt sunt ipsum.",
"meta": "Ipsum sit eiusmod quis ea do non. Sit ad sunt amet id id aute nisi sunt anim in id ullamco. Commodo ad reprehenderit sint eiusmod amet incididunt do ullamco qui ut anim nostrud id. Ullamco aliquip nulla commodo velit sit aliqua. Ex quis enim adipisicing non excepteur sunt ipsum duis ipsum. Ut amet elit aliqua laborum aliquip ex nostrud veniam sint in deserunt. Ea id voluptate qui non sunt mollit voluptate sint.",
- "ips": [
- "57.29.209.151"
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+ "ips": ["57.29.209.151"]
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{
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"vector": "Deserunt cupidatat id nostrud pariatur consectetur proident exercitation esse tempor incididunt. Elit qui laborum et exercitation labore. Non consectetur excepteur quis aliqua tempor aute. Est pariatur veniam incididunt aliqua exercitation dolore ex ea sint sunt. Velit Lorem non anim deserunt irure ad aute qui occaecat sint laboris occaecat.",
"meta": "Nostrud consectetur veniam aute irure. Proident labore deserunt nostrud labore esse. Aliqua occaecat enim sint laboris velit. Commodo excepteur exercitation aute incididunt eu irure ut nostrud. Amet ea ad quis pariatur.",
- "ips": [
- "75.1.246.199"
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+ "ips": ["75.1.246.199"]
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"meta": "Culpa tempor commodo quis ipsum do commodo exercitation et enim. Aliquip sit ad do consequat nisi veniam magna incididunt dolor mollit mollit. Dolore enim velit sit adipisicing esse enim dolore nulla aliqua et commodo id. Duis quis deserunt ex sunt.",
- "ips": [
- "180.250.237.201"
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+ "ips": ["180.250.237.201"]
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{
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"vector": "Incididunt aute proident qui do nisi id commodo sit aliqua mollit tempor mollit. Ipsum anim sunt laborum sunt in laboris aute. Cupidatat pariatur ipsum tempor ipsum sint officia. Adipisicing esse proident excepteur ea id sit. Labore sit officia id esse proident occaecat non velit anim irure ullamco nisi velit est. Ex enim deserunt adipisicing aliqua duis pariatur adipisicing dolor quis commodo ex sint anim commodo.",
"meta": "Enim magna culpa voluptate duis. Veniam sunt ullamco nostrud ex ea non consectetur sunt ad nulla do incididunt et ea. Dolor reprehenderit proident sunt enim pariatur minim ullamco culpa enim. Irure et pariatur sunt excepteur id ea qui deserunt commodo ea est aliquip labore. Ut aliquip quis mollit qui pariatur duis elit velit magna reprehenderit minim. Do anim proident aute labore qui quis. Nulla et Lorem mollit veniam reprehenderit do nulla anim sit consequat.",
- "ips": [
- "60.160.42.200"
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+ "ips": ["60.160.42.200"]
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{
"uuid": "a1802817-6dc4-4838-8c34-0094deffcc8f",
"vector": "Dolore nulla sit enim est qui consequat pariatur quis qui tempor ea ipsum. Veniam amet dolor deserunt in anim commodo tempor. Fugiat non adipisicing esse culpa dolor tempor velit anim. Deserunt laboris eu irure culpa consectetur id dolore dolore mollit ut duis amet. Irure pariatur sint enim consectetur nulla dolor ea velit duis fugiat consectetur Lorem eu deserunt.",
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- "ips": [
- "59.40.69.90"
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+ "ips": ["59.40.69.90"]
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- "ips": [
- "209.175.27.58"
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+ "ips": ["209.175.27.58"]
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{
"uuid": "05dc462e-65e8-4766-9a49-5fe54b7bbfa4",
"vector": "Ad labore ullamco voluptate reprehenderit ad sunt sunt excepteur proident. Nulla ex ut sint commodo elit amet Lorem magna sint. Exercitation sit et deserunt reprehenderit Lorem ex dolore ut.",
"meta": "Ut dolore sint tempor officia. Excepteur velit pariatur sint excepteur dolor sit. Officia velit ipsum sint proident dolore.",
- "ips": [
- "226.94.81.59"
- ]
+ "ips": ["226.94.81.59"]
},
{
"uuid": "7ed16967-500b-4768-a9ff-a7bff718ebf2",
"vector": "Aliqua eiusmod veniam deserunt minim aliqua anim in. Eu fugiat adipisicing labore dolor reprehenderit sit ex aliquip. Enim excepteur esse incididunt qui ipsum ut officia ex aliquip et mollit eu officia ea.",
"meta": "Enim dolore nostrud ea officia incididunt velit esse officia commodo est. Cupidatat eu reprehenderit do nostrud culpa aute qui culpa mollit nostrud veniam est elit Lorem. Cupidatat labore laborum est ullamco do tempor est deserunt culpa laborum cupidatat culpa. Proident amet cillum dolore occaecat. Ea anim do aute nulla reprehenderit irure magna. Commodo nulla ad tempor fugiat.",
- "ips": [
- "229.122.78.253"
- ]
+ "ips": ["229.122.78.253"]
},
{
"uuid": "dfd349b1-9ce1-49d1-9662-418eaa0748ee",
"vector": "Deserunt ullamco reprehenderit sint esse dolore minim enim excepteur sunt. Eu tempor reprehenderit aliquip nostrud. Labore deserunt cillum ad eu aute Lorem ea Lorem qui. In do culpa quis enim anim elit eiusmod et cupidatat.",
"meta": "Fugiat quis labore culpa mollit incididunt dolore commodo labore minim elit. Consequat enim fugiat duis dolore proident. Id dolore duis ipsum ut. Ex ad sit mollit ad sint sit ipsum officia sit quis ut proident elit cupidatat.",
- "ips": [
- "205.230.164.232"
- ]
+ "ips": ["205.230.164.232"]
},
{
"uuid": "e6abe759-9e63-4cc2-8cbc-696d84d94620",
"vector": "Fugiat ut in deserunt esse excepteur cillum commodo do anim commodo. Sint veniam non enim labore laborum. Ullamco minim sunt tempor cillum ex ex aliqua ad id esse enim irure. Est nisi proident veniam dolor et fugiat commodo irure. Cillum ad laborum esse ut.",
"meta": "Eiusmod nostrud reprehenderit magna occaecat aute aute. Pariatur Lorem qui minim non irure non ut non. Minim elit aliquip laboris incididunt.",
- "ips": [
- "155.78.36.98"
- ]
+ "ips": ["155.78.36.98"]
},
{
"uuid": "abbe63fd-7d35-4b15-83b3-12589697663b",
"vector": "Nulla labore veniam do laboris enim laboris cillum veniam sunt sint minim in. Enim esse magna labore ad consectetur id esse commodo ex. Occaecat ullamco nulla excepteur sit quis non. Cillum quis nisi ea elit dolor ad duis laborum eiusmod anim aliquip. Irure culpa ullamco excepteur fugiat nulla veniam aliquip enim officia qui. Nostrud minim do ipsum anim minim do esse.",
"meta": "Veniam eu consectetur minim aliquip. Ex dolor anim excepteur veniam voluptate quis tempor. Mollit magna labore do est et fugiat ad aliquip ullamco tempor fugiat officia veniam. Exercitation veniam ipsum sunt consectetur Lorem mollit voluptate nostrud. Culpa aute sint ad et tempor. Exercitation est veniam id ad Lorem enim minim. Aliquip exercitation labore sunt fugiat magna ex consequat anim est.",
- "ips": [
- "200.49.67.120"
- ]
+ "ips": ["200.49.67.120"]
},
{
"uuid": "5f8d25d0-bafb-4600-b69f-084b7e250986",
"vector": "Mollit anim est dolore ipsum voluptate est excepteur. Proident non ex pariatur consequat sunt eu minim velit deserunt nostrud occaecat mollit. Eiusmod aliqua nostrud sint amet tempor labore mollit excepteur excepteur aliquip sunt. Non duis minim adipisicing incididunt aute laborum. Quis id consectetur nulla aliquip dolore cupidatat consectetur officia. Sit tempor minim do adipisicing consequat elit laborum mollit.",
"meta": "Deserunt cillum exercitation tempor officia amet enim sit sunt. Aliqua laborum ad excepteur sunt in sunt. Commodo sit veniam in laborum est ut veniam minim. Eu do nostrud voluptate do. Qui cillum adipisicing anim proident irure nulla aliquip reprehenderit veniam sit esse. Occaecat id culpa Lorem aliqua aliquip nostrud enim eiusmod pariatur incididunt ut eiusmod ex commodo. Eu est tempor laborum aute magna amet ex veniam mollit.",
- "ips": [
- "108.201.167.239"
- ]
+ "ips": ["108.201.167.239"]
},
{
"uuid": "5b3509b5-e1cf-4431-8b81-1c8229605424",
"vector": "Exercitation sint Lorem culpa minim in quis aliquip cillum deserunt dolor nostrud sint. Incididunt eu in esse sunt nulla ut. Aliquip velit consectetur non est reprehenderit proident ipsum occaecat adipisicing officia Lorem nisi et. Est occaecat velit aliquip quis fugiat. Labore commodo quis tempor ad excepteur.",
"meta": "Officia amet nulla cupidatat nostrud nisi commodo laboris magna eiusmod aliqua. Cillum tempor Lorem esse anim culpa duis aute consectetur adipisicing eiusmod proident elit dolor. Velit laborum incididunt laborum anim.",
- "ips": [
- "253.72.192.63"
- ]
+ "ips": ["253.72.192.63"]
},
{
"uuid": "7a248bf9-d1aa-4bd9-8b22-773a5cfc53ea",
"vector": "Veniam sint officia aliqua magna tempor pariatur consectetur qui fugiat commodo velit. Enim deserunt officia non magna culpa. Nisi dolor pariatur ex velit ut sunt veniam sint minim occaecat nulla duis. Eiusmod irure exercitation in qui. Nostrud nulla minim velit et ex velit incididunt enim officia minim labore sunt.",
"meta": "Sint quis exercitation nisi irure dolore occaecat laborum ullamco magna adipisicing. Commodo id ipsum ullamco velit do in aliqua esse exercitation quis aliquip sit. Id pariatur nisi pariatur sunt sint do excepteur excepteur excepteur labore occaecat ea non nostrud. Incididunt culpa id ex aliqua nostrud id sint consectetur aliqua proident quis veniam in officia. Deserunt consectetur ea non veniam proident anim labore laborum voluptate veniam.",
- "ips": [
- "185.173.158.47"
- ]
+ "ips": ["185.173.158.47"]
},
{
"uuid": "3d299fb2-1aaa-4309-a2e2-a95940f88f86",
"vector": "Fugiat reprehenderit fugiat non non adipisicing laboris veniam ut aute pariatur dolore cupidatat ex duis. Do incididunt ipsum esse officia eu enim dolor mollit adipisicing cillum. Cillum consequat duis quis laborum aliqua officia nostrud. Voluptate sunt ea sit labore. Proident pariatur enim duis ullamco dolore nulla minim minim esse laboris est mollit do. Aliquip nisi eiusmod qui laboris proident exercitation anim commodo laborum nostrud incididunt consequat do.",
"meta": "Velit ipsum exercitation amet anim fugiat deserunt nisi id id. Voluptate veniam sint pariatur ipsum non minim qui ut magna deserunt ut minim aliquip dolore. Cillum amet ullamco enim amet mollit voluptate. Amet ipsum officia dolor adipisicing. Minim duis labore laborum reprehenderit ullamco irure veniam qui nisi. Amet do non sint irure aliqua cillum non consequat elit consectetur. Est fugiat ea aliquip aliquip qui aliquip dolor duis sunt excepteur Lorem officia id amet.",
- "ips": [
- "113.155.251.58"
- ]
+ "ips": ["113.155.251.58"]
}
]
diff --git a/k8s/metrics/otel/README.md b/k8s/metrics/otel/README.md
index 6b9c6a0961..fdad192253 100644
--- a/k8s/metrics/otel/README.md
+++ b/k8s/metrics/otel/README.md
@@ -18,40 +18,40 @@ Vald ===> OpenTelemetry Collector ====
1. To deploy the operator in a Kubernetes Cluster, we first need to deploy [cert-manager](https://cert-manager.io/docs/installation/).
- ```sh
- make k8s/external/cert-manager/deploy
- ```
+ ```sh
+ make k8s/external/cert-manager/deploy
+ ```
2. Deploy Jaeger and Prometheus to export traces and metrics from OpenTelemetry Collector.
- - Deploy Jaeger
+ - Deploy Jaeger
- ```sh
- make k8s/metrics/jaeger/deploy
- ```
+ ```sh
+ make k8s/metrics/jaeger/deploy
+ ```
- - Deploy Prometheus
+ - Deploy Prometheus
- ```sh
- make k8s/metrics/prometheus/operator/deploy
- ```
+ ```sh
+ make k8s/metrics/prometheus/operator/deploy
+ ```
3. Deploy Grafana and dashboard to visualize the metrics.
- ```sh
- make k8s/metrics/grafana/deploy
- ```
+ ```sh
+ make k8s/metrics/grafana/deploy
+ ```
4. Deploy OpenTelemetry Operator and Collector.
- - Deploy OpenTelemetry Operator
+ - Deploy OpenTelemetry Operator
- ```sh
- make k8s/otel/operator/install
- ```
+ ```sh
+ make k8s/otel/operator/install
+ ```
- - Deploy OpenTelemetry Collector
+ - Deploy OpenTelemetry Collector
- ```sh
- make k8s/otel/collector/install
- ```
+ ```sh
+ make k8s/otel/collector/install
+ ```
diff --git a/k8s/metrics/pyroscope/README.md b/k8s/metrics/pyroscope/README.md
index 995c08bad9..9afcb2646f 100644
--- a/k8s/metrics/pyroscope/README.md
+++ b/k8s/metrics/pyroscope/README.md
@@ -2,7 +2,7 @@
This is the mafests to deploy pyroscope server and pyroscope agent.
-The pyroscope server scrapes pprof data.
+The pyroscope server scrapes pprof data.
Which pod data to scrape is determined by `podAnnotations`.
diff --git a/renovate.json b/renovate.json
index f45d8f110c..4f39080e47 100755
--- a/renovate.json
+++ b/renovate.json
@@ -1,5 +1,3 @@
{
- "extends": [
- "config:base"
- ]
+ "extends": ["config:base"]
}
diff --git a/tests/chaos/chart/README.md b/tests/chaos/chart/README.md
index b44b1506ab..0a5dbdc2cb 100644
--- a/tests/chaos/chart/README.md
+++ b/tests/chaos/chart/README.md
@@ -6,38 +6,39 @@ A Helm chart for testing Vald using Chaos Mesh.
## Values
-| Key | Type | Default | Description |
-|-----|------|---------|-------------|
-| networkChaos.bandwidth.buffer | int | `10000` | |
-| networkChaos.bandwidth.duration | string | `"100ms"` | |
-| networkChaos.bandwidth.enabled | bool | `false` | |
-| networkChaos.bandwidth.limit | int | `100` | |
-| networkChaos.bandwidth.minburst | int | `1000000` | |
-| networkChaos.bandwidth.name | string | `"vald-network-bandwidth"` | |
-| networkChaos.bandwidth.peakrate | int | `1000000` | |
-| networkChaos.bandwidth.rate | string | `"100kbps"` | |
-| networkChaos.bandwidth.schedule | string | `"*/3 * * * * *"` | |
-| networkChaos.bandwidth.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
-| networkChaos.bandwidth.selector.namespaces[0] | string | `"default"` | |
-| networkChaos.partition.duration | string | `"100ms"` | |
-| networkChaos.partition.enabled | bool | `false` | |
-| networkChaos.partition.name | string | `"vald-network-partition"` | |
-| networkChaos.partition.schedule | string | `"*/3 * * * * *"` | |
-| networkChaos.partition.selector.labelSelectors."app.kubernetes.io/component" | string | `"agent"` | |
-| networkChaos.partition.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
-| networkChaos.partition.selector.namespaces[0] | string | `"default"` | |
-| podChaos.failure.duration | string | `"10s"` | |
-| podChaos.failure.enabled | bool | `false` | |
-| podChaos.failure.name | string | `"vald-pod-failure"` | |
-| podChaos.failure.schedule | string | `"*/1 * * * *"` | |
-| podChaos.failure.selector.labelSelectors."app.kubernetes.io/component" | string | `"agent"` | |
-| podChaos.failure.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
-| podChaos.failure.selector.namespaces[0] | string | `"default"` | |
-| podChaos.kill.enabled | bool | `false` | |
-| podChaos.kill.name | string | `"vald-pod-kill"` | |
-| podChaos.kill.schedule | string | `"*/2 * * * *"` | |
-| podChaos.kill.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
-| podChaos.kill.selector.namespaces[0] | string | `"default"` | |
+| Key | Type | Default | Description |
+| ---------------------------------------------------------------------------- | ------ | -------------------------- | ----------- |
+| networkChaos.bandwidth.buffer | int | `10000` | |
+| networkChaos.bandwidth.duration | string | `"100ms"` | |
+| networkChaos.bandwidth.enabled | bool | `false` | |
+| networkChaos.bandwidth.limit | int | `100` | |
+| networkChaos.bandwidth.minburst | int | `1000000` | |
+| networkChaos.bandwidth.name | string | `"vald-network-bandwidth"` | |
+| networkChaos.bandwidth.peakrate | int | `1000000` | |
+| networkChaos.bandwidth.rate | string | `"100kbps"` | |
+| networkChaos.bandwidth.schedule | string | `"*/3 * * * * *"` | |
+| networkChaos.bandwidth.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
+| networkChaos.bandwidth.selector.namespaces[0] | string | `"default"` | |
+| networkChaos.partition.duration | string | `"100ms"` | |
+| networkChaos.partition.enabled | bool | `false` | |
+| networkChaos.partition.name | string | `"vald-network-partition"` | |
+| networkChaos.partition.schedule | string | `"*/3 * * * * *"` | |
+| networkChaos.partition.selector.labelSelectors."app.kubernetes.io/component" | string | `"agent"` | |
+| networkChaos.partition.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
+| networkChaos.partition.selector.namespaces[0] | string | `"default"` | |
+| podChaos.failure.duration | string | `"10s"` | |
+| podChaos.failure.enabled | bool | `false` | |
+| podChaos.failure.name | string | `"vald-pod-failure"` | |
+| podChaos.failure.schedule | string | `"*/1 * * * *"` | |
+| podChaos.failure.selector.labelSelectors."app.kubernetes.io/component" | string | `"agent"` | |
+| podChaos.failure.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
+| podChaos.failure.selector.namespaces[0] | string | `"default"` | |
+| podChaos.kill.enabled | bool | `false` | |
+| podChaos.kill.name | string | `"vald-pod-kill"` | |
+| podChaos.kill.schedule | string | `"*/2 * * * *"` | |
+| podChaos.kill.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
+| podChaos.kill.selector.namespaces[0] | string | `"default"` | |
+
+---
-----------------------------------------------
Autogenerated from chart metadata using [helm-docs v1.11.0](https://github.com/norwoodj/helm-docs/releases/v1.11.0)
If using PV for the backup, PV won't delete automatically.
-
diff --git a/docs/user-guides/filtering-configuration.md b/docs/user-guides/filtering-configuration.md
index db2e6697ff..b2d864b093 100644
--- a/docs/user-guides/filtering-configuration.md
+++ b/docs/user-guides/filtering-configuration.md
@@ -17,48 +17,48 @@ Every filter component should meet Vald's filter gRPC interface.
- The ingress RPC definition
- ```rpc
- service Filter {
- // Represent the RPC to generate the vector.
- rpc GenVector(payload.v1.Object.Blob) returns (payload.v1.Object.Vector) {
- option (google.api.http) = {
- post : "/filter/ingress/object"
- body : "*"
- };
- }
- // Represent the RPC to filter the vector.
- rpc FilterVector(payload.v1.Object.Vector)
- returns (payload.v1.Object.Vector) {
- option (google.api.http) = {
- post : "/filter/ingress/vector"
- body : "*"
- };
- }
+ ```rpc
+ service Filter {
+ // Represent the RPC to generate the vector.
+ rpc GenVector(payload.v1.Object.Blob) returns (payload.v1.Object.Vector) {
+ option (google.api.http) = {
+ post : "/filter/ingress/object"
+ body : "*"
+ };
}
- ```
+ // Represent the RPC to filter the vector.
+ rpc FilterVector(payload.v1.Object.Vector)
+ returns (payload.v1.Object.Vector) {
+ option (google.api.http) = {
+ post : "/filter/ingress/vector"
+ body : "*"
+ };
+ }
+ }
+ ```
- The egress RPC definition
- ```rpc
- service Filter {
- // Represent the RPC to filter the distance.
- rpc FilterDistance(payload.v1.Object.Distance)
- returns (payload.v1.Object.Distance) {
- option (google.api.http) = {
- post : "/filter/egress/distance"
- body : "*"
- };
- }
- // Represent the RPC to filter the vector.
- rpc FilterVector(payload.v1.Object.Vector)
- returns (payload.v1.Object.Vector) {
- option (google.api.http) = {
- post : "/filter/egress/vector"
- body : "*"
- };
- }
+ ```rpc
+ service Filter {
+ // Represent the RPC to filter the distance.
+ rpc FilterDistance(payload.v1.Object.Distance)
+ returns (payload.v1.Object.Distance) {
+ option (google.api.http) = {
+ post : "/filter/egress/distance"
+ body : "*"
+ };
+ }
+ // Represent the RPC to filter the vector.
+ rpc FilterVector(payload.v1.Object.Vector)
+ returns (payload.v1.Object.Vector) {
+ option (google.api.http) = {
+ post : "/filter/egress/vector"
+ body : "*"
+ };
}
- ```
+ }
+ ```
For more details, please refer to [the Vald Filter Gateway document](../overview/component/filter-gateway.md)
@@ -68,6 +68,7 @@ Vald provides the two types of ingress filter components.
These will help you to implement your original filter component.
Please refer to:
+
- [Vald ONNX Ingress Filter](https://github.com/vdaas/vald-onnx-ingress-filter)
- [Vald Tensorflow Ingress Filter](https://github.com/vdaas/vald-tensorflow-ingress-filter)
@@ -76,12 +77,11 @@ Please refer to:
It is easy to enable the filtering feature.
```yaml
-...
+---
gateway:
-...
- filter:
- enabled: true
-...
+---
+filter:
+ enabled: true
```
The Vald Filter gateway connects to the filter component specified in the users' request.
diff --git a/docs/user-guides/operations.md b/docs/user-guides/operations.md
index 8a9b4038b5..d8de2d6466 100644
--- a/docs/user-guides/operations.md
+++ b/docs/user-guides/operations.md
@@ -27,11 +27,11 @@ For example:
If youβre going to deploy Vald on the multi-tenant cluster, please take care of the followings.
- It is recommended to define PriorityClasses for agents not to be evicted.
- - For more info, please visit the page [Pod Priority and Preemption][pod-priority-preemption].
- - If you are using [the Vald chart][vald-helm-chart], PriorityClasses are defined by default.
+ - For more info, please visit the page [Pod Priority and Preemption][pod-priority-preemption].
+ - If you are using [the Vald chart][vald-helm-chart], PriorityClasses are defined by default.
- Defining unique namespaces for each Vald and the other apps are recommended.
- Then, please define ResourceQuotas for the namespace for the other apps to limit their memory usage.
- - For more info, please visit this page, [Resource Quotas][resource-quota].
+ - For more info, please visit this page, [Resource Quotas][resource-quota].
## Monitoring
@@ -93,12 +93,11 @@ This is an example of a custom dashboard. It is based on [our standard dashboard
-
## Upgrading
Our versioning strategy is based on [Semantic Versioning][semver].
Upgrading to a new version, such as minor or major, may require changing your configurations.
-Please read the [CHANGELOG][CHANGELOG] before upgrading.
+Please read the [CHANGELOG][changelog] before upgrading.
### In case of manual deploy
@@ -129,23 +128,17 @@ On the other hand, please update the operator's deployment manually.
After that, please update `image.tag` field in your valdrelease (or `vr`) resource.
The operator will automatically detect the changes and update the deployed Vald cluster.
-
-
-
### References
- [vald-helm-chart][vald-helm-chart]
- [vald-helm-operator-chart][vald-helm-operator-chart]
-- [CHANGELOG][CHANGELOG]
-
+- [CHANGELOG][changelog]
[vald-helm-chart]: https://github.com/vdaas/vald/tree/main/charts/vald
[vald-helm-operator-chart]: https://github.com/vdaas/vald/tree/main/charts/vald-helm-operator
-[CHANGELOG]: https://vald.vdaas.org/docs/release/changelog/
-
+[changelog]: https://vald.vdaas.org/docs/release/changelog/
[pod-priority-preemption]: https://kubernetes.io/docs/concepts/configuration/pod-priority-preemption/
[resource-quota]: https://kubernetes.io/docs/concepts/policy/resource-quotas/
-
[prometheus-io]: https://prometheus.io/
[grafana]: https://grafana.com
[vald-prometheus]: https://github.com/vdaas/vald/tree/main/k8s/metrics/prometheus
diff --git a/hack/CHANGELOG.template.md b/hack/CHANGELOG.template.md
index 38e37a7536..0a9df0b417 100644
--- a/hack/CHANGELOG.template.md
+++ b/hack/CHANGELOG.template.md
@@ -54,6 +54,7 @@
### Documents
+
- [GoDoc](https://pkg.go.dev/github.com/vdaas/vald@{{ version }})
- [Helm Chart Reference](https://github.com/vdaas/vald/blob/{{ version }}/charts/vald/README.md)
- [Helm Operator Chart Reference](https://github.com/vdaas/vald/blob/{{ version }}/charts/vald-helm-operator/README.md)
diff --git a/hack/benchmark/internal/db/nosql/cassandra/testdata.json b/hack/benchmark/internal/db/nosql/cassandra/testdata.json
index b9cf09e865..7afdd00da9 100644
--- a/hack/benchmark/internal/db/nosql/cassandra/testdata.json
+++ b/hack/benchmark/internal/db/nosql/cassandra/testdata.json
@@ -3,488 +3,366 @@
"uuid": "8a8dfec5-2115-45ae-9a07-f8634ff61477",
"vector": "Sint aliqua anim mollit aute veniam non duis ut elit elit dolor occaecat nulla commodo. Lorem id qui nulla sit Lorem occaecat consectetur sunt duis nulla tempor. Irure occaecat nostrud ut cupidatat amet sit Lorem. Labore enim officia est sunt ullamco. Exercitation aute sunt dolor culpa ipsum elit irure fugiat aliqua voluptate occaecat Lorem dolor. Lorem fugiat consequat ipsum adipisicing commodo qui est cillum mollit quis culpa eiusmod.",
"meta": "Eu et id dolore consequat consequat laboris consectetur do. Occaecat occaecat consequat velit dolore dolore nostrud anim culpa. Aliqua incididunt excepteur officia elit officia deserunt dolor aute culpa nostrud veniam.",
- "ips": [
- "8.144.247.69"
- ]
+ "ips": ["8.144.247.69"]
},
{
"uuid": "7b528973-6459-4af3-b0dd-9b6d341294b6",
"vector": "Officia dolor dolore fugiat aliquip pariatur mollit non deserunt. Nulla voluptate laboris eiusmod cupidatat laboris aliquip et mollit in mollit ea nulla. Ad anim ullamco quis veniam deserunt reprehenderit quis velit cupidatat ad nisi. Consectetur non sit reprehenderit nulla officia laboris aliqua nisi reprehenderit velit labore incididunt id. Culpa sit sit aliqua consectetur. Labore voluptate minim consectetur quis aliqua aliqua elit consectetur elit laboris esse pariatur. Ea ut est et consequat minim.",
"meta": "Ipsum veniam anim tempor qui consectetur occaecat fugiat magna exercitation adipisicing excepteur elit proident est. Laborum mollit duis deserunt exercitation adipisicing laboris duis duis mollit sunt est cupidatat dolore. Nostrud laborum tempor voluptate culpa occaecat cillum enim excepteur ut. Reprehenderit ea elit ex eiusmod eiusmod quis consectetur deserunt enim ipsum enim officia et qui.",
- "ips": [
- "251.180.215.252"
- ]
+ "ips": ["251.180.215.252"]
},
{
"uuid": "a109bcc3-cc27-4dcf-a31e-63cade795d29",
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+ "ips": ["108.59.52.87"]
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- "178.72.12.38"
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+ "ips": ["178.72.12.38"]
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+ "ips": ["96.211.93.91"]
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+ "ips": ["100.86.51.200"]
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- "34.57.190.71"
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+ "ips": ["34.57.190.71"]
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+ "ips": ["103.116.240.218"]
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- "15.63.160.66"
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+ "ips": ["15.63.160.66"]
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{
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- "219.138.44.251"
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+ "ips": ["219.138.44.251"]
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- "198.98.227.210"
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+ "ips": ["198.98.227.210"]
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+ "ips": ["90.121.224.191"]
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- "ips": [
- "77.151.18.67"
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+ "ips": ["77.151.18.67"]
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{
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- "ips": [
- "14.70.175.205"
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+ "ips": ["14.70.175.205"]
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{
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- "ips": [
- "216.127.244.240"
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+ "ips": ["216.127.244.240"]
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- "ips": [
- "57.29.209.151"
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+ "ips": ["57.29.209.151"]
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"vector": "Deserunt cupidatat id nostrud pariatur consectetur proident exercitation esse tempor incididunt. Elit qui laborum et exercitation labore. Non consectetur excepteur quis aliqua tempor aute. Est pariatur veniam incididunt aliqua exercitation dolore ex ea sint sunt. Velit Lorem non anim deserunt irure ad aute qui occaecat sint laboris occaecat.",
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- "ips": [
- "75.1.246.199"
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+ "ips": ["75.1.246.199"]
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"meta": "Culpa tempor commodo quis ipsum do commodo exercitation et enim. Aliquip sit ad do consequat nisi veniam magna incididunt dolor mollit mollit. Dolore enim velit sit adipisicing esse enim dolore nulla aliqua et commodo id. Duis quis deserunt ex sunt.",
- "ips": [
- "180.250.237.201"
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+ "ips": ["180.250.237.201"]
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"vector": "Incididunt aute proident qui do nisi id commodo sit aliqua mollit tempor mollit. Ipsum anim sunt laborum sunt in laboris aute. Cupidatat pariatur ipsum tempor ipsum sint officia. Adipisicing esse proident excepteur ea id sit. Labore sit officia id esse proident occaecat non velit anim irure ullamco nisi velit est. Ex enim deserunt adipisicing aliqua duis pariatur adipisicing dolor quis commodo ex sint anim commodo.",
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- "ips": [
- "60.160.42.200"
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+ "ips": ["60.160.42.200"]
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- "ips": [
- "59.40.69.90"
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+ "ips": ["59.40.69.90"]
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- "ips": [
- "209.175.27.58"
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+ "ips": ["209.175.27.58"]
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- "ips": [
- "201.58.203.66"
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+ "ips": ["201.58.203.66"]
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- "ips": [
- "35.102.175.176"
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+ "ips": ["35.102.175.176"]
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"meta": "Fugiat quis labore culpa mollit incididunt dolore commodo labore minim elit. Consequat enim fugiat duis dolore proident. Id dolore duis ipsum ut. Ex ad sit mollit ad sint sit ipsum officia sit quis ut proident elit cupidatat.",
- "ips": [
- "205.230.164.232"
- ]
+ "ips": ["205.230.164.232"]
},
{
"uuid": "e6abe759-9e63-4cc2-8cbc-696d84d94620",
"vector": "Fugiat ut in deserunt esse excepteur cillum commodo do anim commodo. Sint veniam non enim labore laborum. Ullamco minim sunt tempor cillum ex ex aliqua ad id esse enim irure. Est nisi proident veniam dolor et fugiat commodo irure. Cillum ad laborum esse ut.",
"meta": "Eiusmod nostrud reprehenderit magna occaecat aute aute. Pariatur Lorem qui minim non irure non ut non. Minim elit aliquip laboris incididunt.",
- "ips": [
- "155.78.36.98"
- ]
+ "ips": ["155.78.36.98"]
},
{
"uuid": "abbe63fd-7d35-4b15-83b3-12589697663b",
"vector": "Nulla labore veniam do laboris enim laboris cillum veniam sunt sint minim in. Enim esse magna labore ad consectetur id esse commodo ex. Occaecat ullamco nulla excepteur sit quis non. Cillum quis nisi ea elit dolor ad duis laborum eiusmod anim aliquip. Irure culpa ullamco excepteur fugiat nulla veniam aliquip enim officia qui. Nostrud minim do ipsum anim minim do esse.",
"meta": "Veniam eu consectetur minim aliquip. Ex dolor anim excepteur veniam voluptate quis tempor. Mollit magna labore do est et fugiat ad aliquip ullamco tempor fugiat officia veniam. Exercitation veniam ipsum sunt consectetur Lorem mollit voluptate nostrud. Culpa aute sint ad et tempor. Exercitation est veniam id ad Lorem enim minim. Aliquip exercitation labore sunt fugiat magna ex consequat anim est.",
- "ips": [
- "200.49.67.120"
- ]
+ "ips": ["200.49.67.120"]
},
{
"uuid": "5f8d25d0-bafb-4600-b69f-084b7e250986",
"vector": "Mollit anim est dolore ipsum voluptate est excepteur. Proident non ex pariatur consequat sunt eu minim velit deserunt nostrud occaecat mollit. Eiusmod aliqua nostrud sint amet tempor labore mollit excepteur excepteur aliquip sunt. Non duis minim adipisicing incididunt aute laborum. Quis id consectetur nulla aliquip dolore cupidatat consectetur officia. Sit tempor minim do adipisicing consequat elit laborum mollit.",
"meta": "Deserunt cillum exercitation tempor officia amet enim sit sunt. Aliqua laborum ad excepteur sunt in sunt. Commodo sit veniam in laborum est ut veniam minim. Eu do nostrud voluptate do. Qui cillum adipisicing anim proident irure nulla aliquip reprehenderit veniam sit esse. Occaecat id culpa Lorem aliqua aliquip nostrud enim eiusmod pariatur incididunt ut eiusmod ex commodo. Eu est tempor laborum aute magna amet ex veniam mollit.",
- "ips": [
- "108.201.167.239"
- ]
+ "ips": ["108.201.167.239"]
},
{
"uuid": "5b3509b5-e1cf-4431-8b81-1c8229605424",
"vector": "Exercitation sint Lorem culpa minim in quis aliquip cillum deserunt dolor nostrud sint. Incididunt eu in esse sunt nulla ut. Aliquip velit consectetur non est reprehenderit proident ipsum occaecat adipisicing officia Lorem nisi et. Est occaecat velit aliquip quis fugiat. Labore commodo quis tempor ad excepteur.",
"meta": "Officia amet nulla cupidatat nostrud nisi commodo laboris magna eiusmod aliqua. Cillum tempor Lorem esse anim culpa duis aute consectetur adipisicing eiusmod proident elit dolor. Velit laborum incididunt laborum anim.",
- "ips": [
- "253.72.192.63"
- ]
+ "ips": ["253.72.192.63"]
},
{
"uuid": "7a248bf9-d1aa-4bd9-8b22-773a5cfc53ea",
"vector": "Veniam sint officia aliqua magna tempor pariatur consectetur qui fugiat commodo velit. Enim deserunt officia non magna culpa. Nisi dolor pariatur ex velit ut sunt veniam sint minim occaecat nulla duis. Eiusmod irure exercitation in qui. Nostrud nulla minim velit et ex velit incididunt enim officia minim labore sunt.",
"meta": "Sint quis exercitation nisi irure dolore occaecat laborum ullamco magna adipisicing. Commodo id ipsum ullamco velit do in aliqua esse exercitation quis aliquip sit. Id pariatur nisi pariatur sunt sint do excepteur excepteur excepteur labore occaecat ea non nostrud. Incididunt culpa id ex aliqua nostrud id sint consectetur aliqua proident quis veniam in officia. Deserunt consectetur ea non veniam proident anim labore laborum voluptate veniam.",
- "ips": [
- "185.173.158.47"
- ]
+ "ips": ["185.173.158.47"]
},
{
"uuid": "3d299fb2-1aaa-4309-a2e2-a95940f88f86",
"vector": "Fugiat reprehenderit fugiat non non adipisicing laboris veniam ut aute pariatur dolore cupidatat ex duis. Do incididunt ipsum esse officia eu enim dolor mollit adipisicing cillum. Cillum consequat duis quis laborum aliqua officia nostrud. Voluptate sunt ea sit labore. Proident pariatur enim duis ullamco dolore nulla minim minim esse laboris est mollit do. Aliquip nisi eiusmod qui laboris proident exercitation anim commodo laborum nostrud incididunt consequat do.",
"meta": "Velit ipsum exercitation amet anim fugiat deserunt nisi id id. Voluptate veniam sint pariatur ipsum non minim qui ut magna deserunt ut minim aliquip dolore. Cillum amet ullamco enim amet mollit voluptate. Amet ipsum officia dolor adipisicing. Minim duis labore laborum reprehenderit ullamco irure veniam qui nisi. Amet do non sint irure aliqua cillum non consequat elit consectetur. Est fugiat ea aliquip aliquip qui aliquip dolor duis sunt excepteur Lorem officia id amet.",
- "ips": [
- "113.155.251.58"
- ]
+ "ips": ["113.155.251.58"]
}
]
diff --git a/k8s/metrics/otel/README.md b/k8s/metrics/otel/README.md
index 6b9c6a0961..fdad192253 100644
--- a/k8s/metrics/otel/README.md
+++ b/k8s/metrics/otel/README.md
@@ -18,40 +18,40 @@ Vald ===> OpenTelemetry Collector ====
1. To deploy the operator in a Kubernetes Cluster, we first need to deploy [cert-manager](https://cert-manager.io/docs/installation/).
- ```sh
- make k8s/external/cert-manager/deploy
- ```
+ ```sh
+ make k8s/external/cert-manager/deploy
+ ```
2. Deploy Jaeger and Prometheus to export traces and metrics from OpenTelemetry Collector.
- - Deploy Jaeger
+ - Deploy Jaeger
- ```sh
- make k8s/metrics/jaeger/deploy
- ```
+ ```sh
+ make k8s/metrics/jaeger/deploy
+ ```
- - Deploy Prometheus
+ - Deploy Prometheus
- ```sh
- make k8s/metrics/prometheus/operator/deploy
- ```
+ ```sh
+ make k8s/metrics/prometheus/operator/deploy
+ ```
3. Deploy Grafana and dashboard to visualize the metrics.
- ```sh
- make k8s/metrics/grafana/deploy
- ```
+ ```sh
+ make k8s/metrics/grafana/deploy
+ ```
4. Deploy OpenTelemetry Operator and Collector.
- - Deploy OpenTelemetry Operator
+ - Deploy OpenTelemetry Operator
- ```sh
- make k8s/otel/operator/install
- ```
+ ```sh
+ make k8s/otel/operator/install
+ ```
- - Deploy OpenTelemetry Collector
+ - Deploy OpenTelemetry Collector
- ```sh
- make k8s/otel/collector/install
- ```
+ ```sh
+ make k8s/otel/collector/install
+ ```
diff --git a/k8s/metrics/pyroscope/README.md b/k8s/metrics/pyroscope/README.md
index 995c08bad9..9afcb2646f 100644
--- a/k8s/metrics/pyroscope/README.md
+++ b/k8s/metrics/pyroscope/README.md
@@ -2,7 +2,7 @@
This is the mafests to deploy pyroscope server and pyroscope agent.
-The pyroscope server scrapes pprof data.
+The pyroscope server scrapes pprof data.
Which pod data to scrape is determined by `podAnnotations`.
diff --git a/renovate.json b/renovate.json
index f45d8f110c..4f39080e47 100755
--- a/renovate.json
+++ b/renovate.json
@@ -1,5 +1,3 @@
{
- "extends": [
- "config:base"
- ]
+ "extends": ["config:base"]
}
diff --git a/tests/chaos/chart/README.md b/tests/chaos/chart/README.md
index b44b1506ab..0a5dbdc2cb 100644
--- a/tests/chaos/chart/README.md
+++ b/tests/chaos/chart/README.md
@@ -6,38 +6,39 @@ A Helm chart for testing Vald using Chaos Mesh.
## Values
-| Key | Type | Default | Description |
-|-----|------|---------|-------------|
-| networkChaos.bandwidth.buffer | int | `10000` | |
-| networkChaos.bandwidth.duration | string | `"100ms"` | |
-| networkChaos.bandwidth.enabled | bool | `false` | |
-| networkChaos.bandwidth.limit | int | `100` | |
-| networkChaos.bandwidth.minburst | int | `1000000` | |
-| networkChaos.bandwidth.name | string | `"vald-network-bandwidth"` | |
-| networkChaos.bandwidth.peakrate | int | `1000000` | |
-| networkChaos.bandwidth.rate | string | `"100kbps"` | |
-| networkChaos.bandwidth.schedule | string | `"*/3 * * * * *"` | |
-| networkChaos.bandwidth.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
-| networkChaos.bandwidth.selector.namespaces[0] | string | `"default"` | |
-| networkChaos.partition.duration | string | `"100ms"` | |
-| networkChaos.partition.enabled | bool | `false` | |
-| networkChaos.partition.name | string | `"vald-network-partition"` | |
-| networkChaos.partition.schedule | string | `"*/3 * * * * *"` | |
-| networkChaos.partition.selector.labelSelectors."app.kubernetes.io/component" | string | `"agent"` | |
-| networkChaos.partition.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
-| networkChaos.partition.selector.namespaces[0] | string | `"default"` | |
-| podChaos.failure.duration | string | `"10s"` | |
-| podChaos.failure.enabled | bool | `false` | |
-| podChaos.failure.name | string | `"vald-pod-failure"` | |
-| podChaos.failure.schedule | string | `"*/1 * * * *"` | |
-| podChaos.failure.selector.labelSelectors."app.kubernetes.io/component" | string | `"agent"` | |
-| podChaos.failure.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
-| podChaos.failure.selector.namespaces[0] | string | `"default"` | |
-| podChaos.kill.enabled | bool | `false` | |
-| podChaos.kill.name | string | `"vald-pod-kill"` | |
-| podChaos.kill.schedule | string | `"*/2 * * * *"` | |
-| podChaos.kill.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
-| podChaos.kill.selector.namespaces[0] | string | `"default"` | |
+| Key | Type | Default | Description |
+| ---------------------------------------------------------------------------- | ------ | -------------------------- | ----------- |
+| networkChaos.bandwidth.buffer | int | `10000` | |
+| networkChaos.bandwidth.duration | string | `"100ms"` | |
+| networkChaos.bandwidth.enabled | bool | `false` | |
+| networkChaos.bandwidth.limit | int | `100` | |
+| networkChaos.bandwidth.minburst | int | `1000000` | |
+| networkChaos.bandwidth.name | string | `"vald-network-bandwidth"` | |
+| networkChaos.bandwidth.peakrate | int | `1000000` | |
+| networkChaos.bandwidth.rate | string | `"100kbps"` | |
+| networkChaos.bandwidth.schedule | string | `"*/3 * * * * *"` | |
+| networkChaos.bandwidth.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
+| networkChaos.bandwidth.selector.namespaces[0] | string | `"default"` | |
+| networkChaos.partition.duration | string | `"100ms"` | |
+| networkChaos.partition.enabled | bool | `false` | |
+| networkChaos.partition.name | string | `"vald-network-partition"` | |
+| networkChaos.partition.schedule | string | `"*/3 * * * * *"` | |
+| networkChaos.partition.selector.labelSelectors."app.kubernetes.io/component" | string | `"agent"` | |
+| networkChaos.partition.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
+| networkChaos.partition.selector.namespaces[0] | string | `"default"` | |
+| podChaos.failure.duration | string | `"10s"` | |
+| podChaos.failure.enabled | bool | `false` | |
+| podChaos.failure.name | string | `"vald-pod-failure"` | |
+| podChaos.failure.schedule | string | `"*/1 * * * *"` | |
+| podChaos.failure.selector.labelSelectors."app.kubernetes.io/component" | string | `"agent"` | |
+| podChaos.failure.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
+| podChaos.failure.selector.namespaces[0] | string | `"default"` | |
+| podChaos.kill.enabled | bool | `false` | |
+| podChaos.kill.name | string | `"vald-pod-kill"` | |
+| podChaos.kill.schedule | string | `"*/2 * * * *"` | |
+| podChaos.kill.selector.labelSelectors."app.kubernetes.io/name" | string | `"vald"` | |
+| podChaos.kill.selector.namespaces[0] | string | `"default"` | |
+
+---
-----------------------------------------------
Autogenerated from chart metadata using [helm-docs v1.11.0](https://github.com/norwoodj/helm-docs/releases/v1.11.0)