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[docs] Small changes to website/docs #2900

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4 changes: 2 additions & 2 deletions site/content/how_to/other/tsdb.md
Original file line number Diff line number Diff line change
Expand Up @@ -50,8 +50,8 @@ This would allow users to issue queries that answer questions like:
TODO(rartoul): Discuss the ability to perform limited amounts of aggregation queries here as well.
TODO(rartoul): Discuss ID / tags mutability.

#### Data Points
Each time series in M3DB stores data as a stream of data points in the form of <timestamp, value> tuples. Timestamp resolution can be as granular as individual nanoseconds.
#### Datapoints
Each time series in M3DB stores data as a stream of datapoints in the form of <timestamp, value> tuples. Timestamp resolution can be as granular as individual nanoseconds.
The value portion of the tuple is a Protobuf message that matches the configured namespace schema, which requires that all values in the current time series must also match this schema. This limitation may be lifted in the future.

#### Schema Modeling
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4 changes: 2 additions & 2 deletions site/content/how_to/use_as_tsdb.md
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Expand Up @@ -39,9 +39,9 @@ TODO(rartoul): Discuss the ability to perform limited amounts of aggregation que

TODO(rartoul): Discuss ID / tags mutability.

### Data Points
### Datapoints

Each time series in M3DB stores data as a stream of data points in the form of `<timestamp, value>` tuples. Timestamp resolution can be as granular as individual nanoseconds.
Each time series in M3DB stores data as a stream of datapoints in the form of `<timestamp, value>` tuples. Timestamp resolution can be as granular as individual nanoseconds.

The `value` portion of the tuple is a Protobuf message that matches the configured namespace schema, which requires that all values in the current time series must also match this schema. This limitation may be lifted in the future.

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2 changes: 1 addition & 1 deletion site/content/m3db/architecture/commitlogs.md
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Expand Up @@ -55,7 +55,7 @@ CommitLogMetadata {

Commit log files are compacted via the snapshotting proccess which (if enabled at the namespace level) will snapshot all data in memory into compressed files which have the same structure as the [fileset files](/docs/m3db/architecture/storage) but are stored in a different location. Once these snapshot files are created, then all the commit log files whose data are captured by the snapshot files can be deleted. This can result in significant disk savings for M3DB nodes running with large block sizes and high write volume where the size of the (uncompressed) commit logs can quickly get out of hand.

In addition, since the snapshot files are already compressed, bootstrapping from them is much faster than bootstrapping from raw commit log files because the individual data points don't need to be decoded and then M3TSZ encoded. The M3DB node just needs to read the raw bytes off disk and load them into memory.
In addition, since the snapshot files are already compressed, bootstrapping from them is much faster than bootstrapping from raw commit log files because the individual datapoints don't need to be decoded and then M3TSZ encoded. The M3DB node just needs to read the raw bytes off disk and load them into memory.

### Cleanup

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8 changes: 4 additions & 4 deletions site/content/operational_guide/namespace_configuration.md
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Expand Up @@ -201,15 +201,15 @@ Should match the databases [blocksize](#blocksize) for optimal memory usage.
Can be modified without creating a new namespace: `no`

### aggregationOptions
Options for the Coordinator to use to make decisions around how to aggregate data points.
Options for the Coordinator to use to make decisions around how to aggregate datapoints.

Can be modified without creating a new namespace: `yes`

#### aggregations
One or more set of instructions on how data points should be aggregated within the namespace.
One or more set of instructions on how datapoints should be aggregated within the namespace.

##### aggregated
Whether data points are aggregated.
Whether datapoints are aggregated.

##### attributes
If aggregated is true, specifies how to aggregate data.
Expand All @@ -221,4 +221,4 @@ The time range to aggregate data across.
Options related to downsampling data

###### _all_
Whether to send data points to this namespace. If false, the coordinator will not auto-aggregate incoming data points and data points must be sent the namespace via rules. Defaults to true.
Whether to send datapoints to this namespace. If false, the coordinator will not auto-aggregate incoming datapoints and datapoints must be sent the namespace via rules. Defaults to true.
2 changes: 1 addition & 1 deletion site/static/about/index.html
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Expand Up @@ -154,7 +154,7 @@ <h1 class="line-height-1-2 font-weight-semibold c-mt-8 c-mt-md-6">
<img src="../images/about/component_ingestion.svg" class="d-block mx-auto lazyload" loading="lazy" alt="" />
</div>
<h2 class="title test c-mt-5 font-size-16 font-weight-bold mx-auto text-center">Ingestion &amp; streaming aggregation</h2>
<p class="description line-height-1-3 c-mt-4 font-size-14 font-weight-medium text-center">Ingest and streaming aggregation of metrics with dynamic configuration</p>
<p class="description line-height-1-3 c-mt-4 font-size-14 font-weight-medium text-center">Ingest and aggregate a stream of metrics with dynamic configuration</p>
</div>
</li>

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8 changes: 4 additions & 4 deletions site/static/index.html
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Expand Up @@ -181,7 +181,7 @@ <h3 class="font-size-30 font-weight-bold">

<div class="testimonial-tabs__quote c-mt-10 c-mt-md-8">
<p class="font-size-26 font-weight-semibold line-height-2">
"At a scale of 1.5 million data points ingested per second, it started getting very expensive to monitor our metrics and we had to turn down our replication factor (RF) to 2 on Cassandra. With M3DB, we were able to bring RF back to 3 while also cutting down significantly on hardware / storage costs."
"At a scale of 1.5 million datapoints ingested per second, it started getting very expensive to monitor our metrics and we had to turn down our replication factor (RF) to 2 on Cassandra. With M3DB, we were able to bring RF back to 3 while also cutting down significantly on hardware / storage costs."
</p>
<div class="c-mt-5 d-flex align-items-center">
<img src="../images/home/prateek.jpg" class="person-image object-fit-cover c-mr-3 lazyload" loading="lazy" alt="person-Prateek" />
Expand Down Expand Up @@ -355,7 +355,7 @@ <h3 class="font-size-30 font-weight-bold">

<div class="testimonial-tabs__quote c-mt-10 c-mt-md-8">
<p class="font-size-26 font-weight-semibold line-height-2">
"When querying millions or billions of metrics, you want something flexible and sublinear in speed as the query’s become longer and longer the more distinct values you have. This led us to the creation of M3DB’s inverted index."
"When querying millions or billions of metrics, you want something flexible and sublinear in speed as the queries become longer and longer the more distinct values you have. This led us to the creation of M3DB’s inverted index."
</p>
<div class="c-mt-5 d-flex align-items-center">
<img src="../images/home/rob_skillington.jpg" class="person-image object-fit-cover c-mr-3 lazyload" loading="lazy" alt="person-rob_skillington" />
Expand All @@ -364,7 +364,7 @@ <h4 class="font-size-14 font-weight-semibold">
Rob Skillington
</h4>
<p class="font-size-12">
CTO and Co-Founder of Chraonosphere, Former Tech Lead at Uber
CTO and Co-Founder of Chronosphere, Former Tech Lead at Uber
</p>
</div>
</div>
Expand Down Expand Up @@ -485,7 +485,7 @@ <h3 class="font-size-30 font-weight-bold">

<div class="testimonial-tabs__quote c-mt-10 c-mt-md-8">
<p class="font-size-26 font-weight-semibold line-height-2">
"At a scale of 1.5 million data points ingested per second, it started getting very expensive to monitor our metrics and we had to turn down our replication factor (RF) to 2 on Cassandra. With M3DB, we were able to bring RF back to 3 while also cutting down significantly on hardware / storage costs."
"At a scale of 1.5 million datapoints ingested per second, it started getting very expensive to monitor our metrics and we had to turn down our replication factor (RF) to 2 on Cassandra. With M3DB, we were able to bring RF back to 3 while also cutting down significantly on hardware / storage costs."
</p>
<div class="c-mt-8 c-mt-md-6 d-flex align-items-center">
<img src="../images/home/Prateek.jpg" class="person-image c-mr-3 lazyload" loading="lazy" alt="person-/Prateek" />
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