Move the terms index of _id off-heap.
#52405
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In elastic#42838 we moved the terms index of all fields off-heap except the `_id` field because we were worried it might make indexing slower. In general, the indexing rate is only affected if explicit IDs are used, as otherwise Elasticsearch almost never performs lookups in the terms dictionary for the purpose of indexing. So it's quite wasteful to require the terms index of `_id` to be loaded on-heap for users who have append-only workloads. Furthermore I've been conducting benchmarks when indexing with explicit ids on the http_logs dataset that suggest that the slowdown is low enough that it's probably not worth forcing the terms index to be kept on-heap. Here are some numbers for the median indexing rate in docs/s: | Run | Master | Patch | | --- | ------- | ------- | | 1 | 45851.2 | 46401.4 | | 2 | 45192.6 | 44561.0 | | 3 | 45635.2 | 44137.0 | | 4 | 46435.0 | 44692.8 | | 5 | 45829.0 | 44949.0 | And now heap usage in MB for segments: | Run | Master | Patch | | --- | ------- | -------- | | 1 | 41.1720 | 0.352083 | | 2 | 45.1545 | 0.382534 | | 3 | 41.7746 | 0.381285 | | 4 | 45.3673 | 0.412737 | | 5 | 45.4616 | 0.375063 | Indexing rate decreased by 1.8% on average, while memory usage decreased by more than 100x. The `http_logs` dataset contains small documents and has a simple indexing chain. More complex indexing chains, e.g. with more fields, ingest pipelines, etc. would see an even lower decrease of indexing rate.
jpountz
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Pinging @elastic/es-distributed (:Distributed/Engine) |
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I'm sharing all the Rally outputs for completeness: Master: Patch: |
dnhatn
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Feb 18, 2020
| @@ -84,6 +84,9 @@ | |||
| "[true, false, checksum] but was: " + s); | |||
| } | |||
| }, Property.IndexScope); | |||
| // This setting is undocumented as it is considered as an escape hatch. | |||
| public static final Setting<Boolean> ON_HEAP_ID_TERMS_INDEX = | |||
| Setting.boolSetting("index.force_memory_id_terms_dictinary", false, Property.IndexScope); | |||
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Thanks for catching this, you were not nit-picking!
jpountz
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Backport of elastic#52405 In elastic#42838 we moved the terms index of all fields off-heap except the `_id` field because we were worried it might make indexing slower. In general, the indexing rate is only affected if explicit IDs are used, as otherwise Elasticsearch almost never performs lookups in the terms dictionary for the purpose of indexing. So it's quite wasteful to require the terms index of `_id` to be loaded on-heap for users who have append-only workloads. Furthermore I've been conducting benchmarks when indexing with explicit ids on the http_logs dataset that suggest that the slowdown is low enough that it's probably not worth forcing the terms index to be kept on-heap. Here are some numbers for the median indexing rate in docs/s: | Run | Master | Patch | | --- | ------- | ------- | | 1 | 45851.2 | 46401.4 | | 2 | 45192.6 | 44561.0 | | 3 | 45635.2 | 44137.0 | | 4 | 46435.0 | 44692.8 | | 5 | 45829.0 | 44949.0 | And now heap usage in MB for segments: | Run | Master | Patch | | --- | ------- | -------- | | 1 | 41.1720 | 0.352083 | | 2 | 45.1545 | 0.382534 | | 3 | 41.7746 | 0.381285 | | 4 | 45.3673 | 0.412737 | | 5 | 45.4616 | 0.375063 | Indexing rate decreased by 1.8% on average, while memory usage decreased by more than 100x. The `http_logs` dataset contains small documents and has a simple indexing chain. More complex indexing chains, e.g. with more fields, ingest pipelines, etc. would see an even lower decrease of indexing rate.
sbourke
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Feb 19, 2020
In elastic#42838 we moved the terms index of all fields off-heap except the `_id` field because we were worried it might make indexing slower. In general, the indexing rate is only affected if explicit IDs are used, as otherwise Elasticsearch almost never performs lookups in the terms dictionary for the purpose of indexing. So it's quite wasteful to require the terms index of `_id` to be loaded on-heap for users who have append-only workloads. Furthermore I've been conducting benchmarks when indexing with explicit ids on the http_logs dataset that suggest that the slowdown is low enough that it's probably not worth forcing the terms index to be kept on-heap. Here are some numbers for the median indexing rate in docs/s: | Run | Master | Patch | | --- | ------- | ------- | | 1 | 45851.2 | 46401.4 | | 2 | 45192.6 | 44561.0 | | 3 | 45635.2 | 44137.0 | | 4 | 46435.0 | 44692.8 | | 5 | 45829.0 | 44949.0 | And now heap usage in MB for segments: | Run | Master | Patch | | --- | ------- | -------- | | 1 | 41.1720 | 0.352083 | | 2 | 45.1545 | 0.382534 | | 3 | 41.7746 | 0.381285 | | 4 | 45.3673 | 0.412737 | | 5 | 45.4616 | 0.375063 | Indexing rate decreased by 1.8% on average, while memory usage decreased by more than 100x. The `http_logs` dataset contains small documents and has a simple indexing chain. More complex indexing chains, e.g. with more fields, ingest pipelines, etc. would see an even lower decrease of indexing rate.
This was referenced Feb 20, 2020
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mfussenegger
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Port of elastic/elasticsearch#52405 but excludes the undocumented setting. Relates to #9796
mfussenegger
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Port of elastic/elasticsearch#52405 but excludes the undocumented setting. Relates to #9796
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Port of elastic/elasticsearch#52405 but excludes the undocumented setting. Relates to #9796
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In #42838 we moved the terms index of all fields off-heap except the
_idfield because we were worried it might make indexing slower. Ingeneral, the indexing rate is only affected if explicit IDs are used, as
otherwise Elasticsearch almost never performs lookups in the terms
dictionary for the purpose of indexing. So it's quite wasteful to
require the terms index of
_idto be loaded on-heap for users who haveappend-only workloads. Furthermore I've been conducting benchmarks when
indexing with explicit ids on the http_logs dataset that suggest that
the slowdown is low enough that it's probably not worth forcing the terms
index to be kept on-heap. Here are some numbers for the median indexing
rate in docs/s:
And now heap usage in MB for segments:
Indexing rate decreased by 1.8% on average, while memory usage decreased
by more than 100x.
The
http_logsdataset contains small documents and has a simpleindexing chain. More complex indexing chains, e.g. with more fields,
ingest pipelines, etc. would see an even lower decrease of indexing rate.