Memory efficient xcontent filtering #77154

nik9000 · 2021-09-01T21:47:40Z

I found myself needing support for something like filter_path on
XContentParser. It was simple enough to plug it in so I did. Then I
realized that it might offer more memory efficient source filtering
(#25168) so I put together a quick benchmark comparing the source
filtering that we do in _search. This doesn't actually make the change
to the source fetching phase - it just benchmarks how it'd be.

Filtering using the parser is about 33% faster than how we filter now
when you select a single field from a 300 byte document:

Benchmark                                          (excludes)  (includes)  (source)  Mode  Cnt     Score    Error  Units
FetchSourcePhaseBenchmark.filterObjects                           message     short  avgt    5  2360.342 ±  4.715  ns/op
FetchSourcePhaseBenchmark.filterXContentOnBuilder                 message     short  avgt    5  2010.278 ± 15.042  ns/op
FetchSourcePhaseBenchmark.filterXContentOnParser                  message     short  avgt    5  1588.446 ± 18.593  ns/op

The top line is the way we filter now. The middle line is adding a
filter to XContentBuilder - something we can do right now without any
of my plumbing work. The bottom line is filtering on the parser,
requiring all the new plumbing.

This isn't particularly impresive. 33% sounds great! But 700
nanoseconds per document isn't going to cut into anyone's search times.
If you fetch a thousand docuents that's .7 milliseconds of savings.

But we mostly advise folks to use source filtering on fetch when the
source is large and you only want a small part of it. So I tried when
the source is about 4.3kb and you want a single field:

Benchmark                                          (excludes)  (includes)      (source)  Mode  Cnt     Score     Error  Units
FetchSourcePhaseBenchmark.filterObjects                           message  one_4k_field  avgt    5  5957.128 ± 117.402  ns/op
FetchSourcePhaseBenchmark.filterXContentOnBuilder                 message  one_4k_field  avgt    5  4999.073 ±  96.003  ns/op
FetchSourcePhaseBenchmark.filterXContentonParser                  message  one_4k_field  avgt    5  3261.478 ±  48.879  ns/op

That's 45% faster. Put another way, 2.7 microseconds a document. Not
bad!

But have a look at how things come out when you want a single field from
a 4 megabyte document:

Benchmark                                          (excludes)  (includes)      (source)  Mode  Cnt        Score        Error  Units
FetchSourcePhaseBenchmark.filterObjects                           message  one_4m_field  avgt    5  8266343.036 ± 176197.077  ns/op
FetchSourcePhaseBenchmark.filterXContentOnBuilder                 message  one_4m_field  avgt    5  6227560.013 ±  68306.318  ns/op
FetchSourcePhaseBenchmark.filterXContentonParser                  message  one_4m_field  avgt    5  1617153.472 ±  80164.547  ns/op

These documents are very large. I've encountered documents like them in
real life, but they've always been the outlier for me. But a 6.5
millisecond per document savings ain't anything to sneeze at.

Take a look at what you get when I turn on gc metrics:

FetchSourcePhaseBenchmark.filterObjects                          message  one_4m_field  avgt    5   7036097.561 ±  84721.312   ns/op
FetchSourcePhaseBenchmark.filterObjects:·gc.alloc.rate           message  one_4m_field  avgt    5      2166.613 ±     25.975  MB/sec
FetchSourcePhaseBenchmark.filterXContentOnBuilder                message  one_4m_field  avgt    5   6104595.992 ±  55445.508   ns/op
FetchSourcePhaseBenchmark.filterXContentOnBuilder:·gc.alloc.rate message  one_4m_field  avgt    5      2496.978 ±     22.650  MB/sec
FetchSourcePhaseBenchmark.filterXContentonParser                 message  one_4m_field  avgt    5   1614980.846 ±  31716.956   ns/op
FetchSourcePhaseBenchmark.filterXContentonParser:·gc.alloc.rate  message  one_4m_field  avgt    5         1.755 ±      0.035  MB/sec

I found myself needing support for something like `filter_path` on `XContentParser`. It was simple enough to plug it in so I did. Then I realized that it might offer more memory efficient source filtering (elastic#25168) so I put together a quick benchmark comparing the source filtering that we do in `_search`. Filtering using the parser is about 33% faster than how we filter now when you select a single field from a 300 byte document: ``` Benchmark (excludes) (includes) (source) Mode Cnt Score Error Units FetchSourcePhaseBenchmark.filterObjects message short avgt 5 2360.342 ± 4.715 ns/op FetchSourcePhaseBenchmark.filterXContentOnBuilder message short avgt 5 2010.278 ± 15.042 ns/op FetchSourcePhaseBenchmark.filterXContentOnParser message short avgt 5 1588.446 ± 18.593 ns/op ``` The top line is the way we filter now. The middle line is adding a filter to `XContentBuilder` - something we can do right now without any of my plumbing work. The bottom line is filtering on the parser, requiring all the new plumbing. This isn't particularly impresive. 33% *sounds* great! But 700 nanoseconds per document isn't going to cut into anyone's search times. If you fetch a thousand docuents that's .7 milliseconds of savings. But we mostly advise folks to use source filtering on fetch when the source is large and you only want a small part of it. So I tried when the source is about 4.3kb and you want a single field: ``` Benchmark (excludes) (includes) (source) Mode Cnt Score Error Units FetchSourcePhaseBenchmark.filterObjects message one_4k_field avgt 5 5957.128 ± 117.402 ns/op FetchSourcePhaseBenchmark.filterXContentOnBuilder message one_4k_field avgt 5 4999.073 ± 96.003 ns/op FetchSourcePhaseBenchmark.filterXContentonParser message one_4k_field avgt 5 3261.478 ± 48.879 ns/op ``` That's 45% faster. Put another way, 2.7 microseconds a document. Not bad! But have a look at how things come out when you want a single field from a 4 *megabyte* document: ``` Benchmark (excludes) (includes) (source) Mode Cnt Score Error Units FetchSourcePhaseBenchmark.filterObjects message one_4m_field avgt 5 8266343.036 ± 176197.077 ns/op FetchSourcePhaseBenchmark.filterXContentOnBuilder message one_4m_field avgt 5 6227560.013 ± 68306.318 ns/op FetchSourcePhaseBenchmark.filterXContentonParser message one_4m_field avgt 5 1617153.472 ± 80164.547 ns/op ``` These documents are very large. I've encountered documents like them in real life, but they've always been the outlier for me. But a 6.5 millisecond per document savings ain't anything to sneeze at. Take a look at what you get when I turn on gc metrics: ``` FetchSourcePhaseBenchmark.filterObjects message one_4m_field avgt 5 7036097.561 ± 84721.312 ns/op FetchSourcePhaseBenchmark.filterObjects:·gc.alloc.rate message one_4m_field avgt 5 2166.613 ± 25.975 MB/sec FetchSourcePhaseBenchmark.filterXContentOnBuilder message one_4m_field avgt 5 6104595.992 ± 55445.508 ns/op FetchSourcePhaseBenchmark.filterXContentOnBuilder:·gc.alloc.rate message one_4m_field avgt 5 2496.978 ± 22.650 MB/sec FetchSourcePhaseBenchmark.filterXContentonParser message one_4m_field avgt 5 1614980.846 ± 31716.956 ns/op FetchSourcePhaseBenchmark.filterXContentonParser:·gc.alloc.rate message one_4m_field avgt 5 1.755 ± 0.035 MB/sec ```

nik9000 · 2021-09-01T21:54:15Z

I'm not sure where to go from here. I think the xcontent changes could be useful outside of source filtering and I don't think I really have time to land them into source filtering. As much as I love that sweet sweet memory savings I'd be worried about this for the same reasons that @tlrx were in #25168 (comment). Though I believe the savings from using the filtering on the reader are more substantial than in the linked issue - he's solution over there lines up fairly closely with my filterXContentOnBuilder implementation.

Like he said there - things like highlighting cause you to lose all the savings. Well, most of the savings at least. We could probably make them ride along with the same filtering. But that feels quite complex.

nik9000 · 2021-09-01T21:55:53Z

Keep in mind, these microbenchmarks look good because they are microbenchmarks. I think before we can really know what this'd do we'd have to run it in rally with realistic documents.

nik9000 · 2021-09-03T17:22:59Z

...src/main/java/org/elasticsearch/common/xcontent/support/filtering/FilterPathBasedFilter.java

+        @Override
+        public String toString() {
+            return "NO_MATCHING";
+        }


I added these for easier debugging.

nik9000 · 2021-09-03T17:27:14Z

Like the benchmark implies, I think you could use this during the source fetch phase to speed up the fetches.

I've noticed that you get invalid json when you have a ** in your exclude filter. I imagine that if we plugged this into the fetch phase we'd want to either fix that or use the object based filtering in that case.

elasticmachine · 2021-09-09T14:57:59Z

Pinging @elastic/es-search (Team:Search)

nik9000 · 2021-09-09T14:58:29Z

I've marked this as search/search and draft because it is going in to do search things. It's fairly general though. I'm not sure.

nik9000 · 2021-09-09T19:37:44Z

...a/org/elasticsearch/common/xcontent/support/filtering/AbstractXContentFilteringTestCase.java

+            return filterOnBuilder(sample, includes, excludes);
+        }
+        FilterPath[] includesFilter = FilterPath.compile(includes);
+        return filterOnParser(sample, includesFilter, excludesFilter);


The tests that this enables are super comprehensive. It's kind of a shame to do it randomly though. We could probably use more subclasses or parametersized tests or something to run them all non-randomly. They are plenty fast.

nik9000 · 2021-09-10T14:53:59Z

libs/x-content/src/main/java/org/elasticsearch/common/xcontent/json/JsonXContentParser.java

+        super(xContentRegistry, deprecationHandler, restApiVersion);
+        JsonParser filtered = parser;
+        if (exclude != null) {
+            filtered = new FilteringParserDelegate(filtered, new FilterPathBasedFilter(exclude, false), true, true);


This should probably fail in hasDoubleWildcard. Unless I can figure out a way to make it work!

I added this test.

nik9000 · 2021-09-10T17:06:05Z

The double wildcard on exclude case has been fixed in Jackson 7.13: FasterXML/jackson-core#700

Turns out this is fixed in an unreleased version of jackson!

jaymode · 2021-09-10T17:19:48Z

Drive by comment, this type of filtering may also exclude empty objects and arrays like what happens during response filtering, see #63842. I attempted to fix that but hit issues after opening the PR and never revisited it.

nik9000 · 2021-09-10T18:10:06Z

Drive by comment, this type of filtering may also exclude empty objects and arrays like what happens during response filtering, see #63842. I attempted to fix that but hit issues after opening the PR and never revisited it.

Thanks! I hadn't realized that was a thing. This doesn't actually plug it into the fetch phase - only demonstrate that it'd be faster. It also rigs up the plumbing that I want for tsdb, which is totally my ulterior motive for this. But I'll check if it filters out empty unrelated objects anyway.

nik9000 · 2021-09-10T20:37:51Z

Drive by comment, this type of filtering may also exclude empty objects and arrays like what happens during response filtering, see #63842. I attempted to fix that but hit issues after opening the PR and never revisited it.

Thanks! I hadn't realized that was a thing. This doesn't actually plug it into the fetch phase - only demonstrate that it'd be faster. It also rigs up the plumbing that I want for tsdb, which is totally my ulterior motive for this. But I'll check if it filters out empty unrelated objects anyway.

I've double checked this - the map based filtering that we're using now always throws out empty arrays. This is what the things look like:

curl -uelastic:password -HContent-Type:application/json -XDELETE localhost:9200/test
curl -uelastic:password -HContent-Type:application/json -XPOST localhost:9200/test/_doc?refresh -d'{"array": []}'
curl -uelastic:password -HContent-Type:application/json -XPOST 'localhost:9200/test/_search?pretty&_source=-**.whatever'
...
    "hits" : [
      {
        "_index" : "test",
        "_id" : "3Fly0XsBELTu-qwH8ixM",
        "_score" : 1.0,
        "_source" : { }
      }
    ]
...

So I think this change can only help here.

romseygeek

LGTM

nik9000 · 2021-09-13T15:20:18Z

Thanks @romseygeek! I've pulling in master and will merge when jenkins is happy.

For those following along at home this doesn't change the actual fetch phase - it just plumbs through the ability to filter an XContentParser, tests it, and adds a benchmark showing that it'd be wicked fast if we used it for the fetch phase. I need this off in tsdb land, but I

nik9000 · 2021-09-13T15:39:06Z

The build failures want #77640 and #77643. Those are open but not yet merged.

I found myself needing support for something like `filter_path` on `XContentParser`. It was simple enough to plug it in so I did. Then I realized that it might offer more memory efficient source filtering (elastic#25168) so I put together a quick benchmark comparing the source filtering that we do in `_search`. Filtering using the parser is about 33% faster than how we filter now when you select a single field from a 300 byte document: ``` Benchmark (excludes) (includes) (source) Mode Cnt Score Error Units FetchSourcePhaseBenchmark.filterObjects message short avgt 5 2360.342 ± 4.715 ns/op FetchSourcePhaseBenchmark.filterXContentOnBuilder message short avgt 5 2010.278 ± 15.042 ns/op FetchSourcePhaseBenchmark.filterXContentOnParser message short avgt 5 1588.446 ± 18.593 ns/op ``` The top line is the way we filter now. The middle line is adding a filter to `XContentBuilder` - something we can do right now without any of my plumbing work. The bottom line is filtering on the parser, requiring all the new plumbing. This isn't particularly impresive. 33% *sounds* great! But 700 nanoseconds per document isn't going to cut into anyone's search times. If you fetch a thousand docuents that's .7 milliseconds of savings. But we mostly advise folks to use source filtering on fetch when the source is large and you only want a small part of it. So I tried when the source is about 4.3kb and you want a single field: ``` Benchmark (excludes) (includes) (source) Mode Cnt Score Error Units FetchSourcePhaseBenchmark.filterObjects message one_4k_field avgt 5 5957.128 ± 117.402 ns/op FetchSourcePhaseBenchmark.filterXContentOnBuilder message one_4k_field avgt 5 4999.073 ± 96.003 ns/op FetchSourcePhaseBenchmark.filterXContentonParser message one_4k_field avgt 5 3261.478 ± 48.879 ns/op ``` That's 45% faster. Put another way, 2.7 microseconds a document. Not bad! But have a look at how things come out when you want a single field from a 4 *megabyte* document: ``` Benchmark (excludes) (includes) (source) Mode Cnt Score Error Units FetchSourcePhaseBenchmark.filterObjects message one_4m_field avgt 5 8266343.036 ± 176197.077 ns/op FetchSourcePhaseBenchmark.filterXContentOnBuilder message one_4m_field avgt 5 6227560.013 ± 68306.318 ns/op FetchSourcePhaseBenchmark.filterXContentonParser message one_4m_field avgt 5 1617153.472 ± 80164.547 ns/op ``` These documents are very large. I've encountered documents like them in real life, but they've always been the outlier for me. But a 6.5 millisecond per document savings ain't anything to sneeze at. Take a look at what you get when I turn on gc metrics: ``` FetchSourcePhaseBenchmark.filterObjects message one_4m_field avgt 5 7036097.561 ± 84721.312 ns/op FetchSourcePhaseBenchmark.filterObjects:·gc.alloc.rate message one_4m_field avgt 5 2166.613 ± 25.975 MB/sec FetchSourcePhaseBenchmark.filterXContentOnBuilder message one_4m_field avgt 5 6104595.992 ± 55445.508 ns/op FetchSourcePhaseBenchmark.filterXContentOnBuilder:·gc.alloc.rate message one_4m_field avgt 5 2496.978 ± 22.650 MB/sec FetchSourcePhaseBenchmark.filterXContentonParser message one_4m_field avgt 5 1614980.846 ± 31716.956 ns/op FetchSourcePhaseBenchmark.filterXContentonParser:·gc.alloc.rate message one_4m_field avgt 5 1.755 ± 0.035 MB/sec ```

* Memory efficient xcontent filtering (backport of #77154) I found myself needing support for something like `filter_path` on `XContentParser`. It was simple enough to plug it in so I did. Then I realized that it might offer more memory efficient source filtering (#25168) so I put together a quick benchmark comparing the source filtering that we do in `_search`. Filtering using the parser is about 33% faster than how we filter now when you select a single field from a 300 byte document: ``` Benchmark (excludes) (includes) (source) Mode Cnt Score Error Units FetchSourcePhaseBenchmark.filterObjects message short avgt 5 2360.342 ± 4.715 ns/op FetchSourcePhaseBenchmark.filterXContentOnBuilder message short avgt 5 2010.278 ± 15.042 ns/op FetchSourcePhaseBenchmark.filterXContentOnParser message short avgt 5 1588.446 ± 18.593 ns/op ``` The top line is the way we filter now. The middle line is adding a filter to `XContentBuilder` - something we can do right now without any of my plumbing work. The bottom line is filtering on the parser, requiring all the new plumbing. This isn't particularly impresive. 33% *sounds* great! But 700 nanoseconds per document isn't going to cut into anyone's search times. If you fetch a thousand docuents that's .7 milliseconds of savings. But we mostly advise folks to use source filtering on fetch when the source is large and you only want a small part of it. So I tried when the source is about 4.3kb and you want a single field: ``` Benchmark (excludes) (includes) (source) Mode Cnt Score Error Units FetchSourcePhaseBenchmark.filterObjects message one_4k_field avgt 5 5957.128 ± 117.402 ns/op FetchSourcePhaseBenchmark.filterXContentOnBuilder message one_4k_field avgt 5 4999.073 ± 96.003 ns/op FetchSourcePhaseBenchmark.filterXContentonParser message one_4k_field avgt 5 3261.478 ± 48.879 ns/op ``` That's 45% faster. Put another way, 2.7 microseconds a document. Not bad! But have a look at how things come out when you want a single field from a 4 *megabyte* document: ``` Benchmark (excludes) (includes) (source) Mode Cnt Score Error Units FetchSourcePhaseBenchmark.filterObjects message one_4m_field avgt 5 8266343.036 ± 176197.077 ns/op FetchSourcePhaseBenchmark.filterXContentOnBuilder message one_4m_field avgt 5 6227560.013 ± 68306.318 ns/op FetchSourcePhaseBenchmark.filterXContentonParser message one_4m_field avgt 5 1617153.472 ± 80164.547 ns/op ``` These documents are very large. I've encountered documents like them in real life, but they've always been the outlier for me. But a 6.5 millisecond per document savings ain't anything to sneeze at. Take a look at what you get when I turn on gc metrics: ``` FetchSourcePhaseBenchmark.filterObjects message one_4m_field avgt 5 7036097.561 ± 84721.312 ns/op FetchSourcePhaseBenchmark.filterObjects:·gc.alloc.rate message one_4m_field avgt 5 2166.613 ± 25.975 MB/sec FetchSourcePhaseBenchmark.filterXContentOnBuilder message one_4m_field avgt 5 6104595.992 ± 55445.508 ns/op FetchSourcePhaseBenchmark.filterXContentOnBuilder:·gc.alloc.rate message one_4m_field avgt 5 2496.978 ± 22.650 MB/sec FetchSourcePhaseBenchmark.filterXContentonParser message one_4m_field avgt 5 1614980.846 ± 31716.956 ns/op FetchSourcePhaseBenchmark.filterXContentonParser:·gc.alloc.rate message one_4m_field avgt 5 1.755 ± 0.035 MB/sec ``` * Fixup benchmark for 7.x

This optimizes the text value extraction from source in categorize_text aggregation. Early measurements indicate that the bulk of the time spent in this aggregation is inflating and deserializing the source. We can optimize this a bit (for larger sources) by only extracting the text field we care about. The main downside here is if there is a sub-agg that requires the source, the that agg will need to extract the entire source again. This should be a rare case. NOTE: opening as draft as measurements need to be done on some realistic data to see if this actually saves us time. This takes advantage of the work done here: #77154

…tic#79099) This optimizes the text value extraction from source in categorize_text aggregation. Early measurements indicate that the bulk of the time spent in this aggregation is inflating and deserializing the source. We can optimize this a bit (for larger sources) by only extracting the text field we care about. The main downside here is if there is a sub-agg that requires the source, the that agg will need to extract the entire source again. This should be a rare case. NOTE: opening as draft as measurements need to be done on some realistic data to see if this actually saves us time. This takes advantage of the work done here: elastic#77154

…es from _source (#79651) Previously created optimizations: - Enabling XContentParser filtering for specific fields: #77154 - Retrieving only specific fields from _source: #79099 Allow significant_text to only parse out the specific field it requires from `_source`. This allows for a minor optimization when `_source` is small, but can be significant (pun intended) when `_source` contains many fields, or other larger fields that `significant_text` doesn't care about. Since `significant_text` does not allow sub-aggs, not caching the parsed `_source` is reasonable.

elasticsearchmachine added the v8.0.0 label Sep 1, 2021

nik9000 changed the title ~~Filter source on read~~ Memory efficient xcontent filtering Sep 1, 2021

nik9000 added 5 commits September 2, 2021 09:14

Rename

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Merge branch 'master' into faster_source_filter

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Merge branch 'master' into faster_source_filter

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Test

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nik9000 requested a review from romseygeek September 3, 2021 17:07

Merge branch 'master' into faster_source_filter

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nik9000 marked this pull request as ready for review September 9, 2021 14:57

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Merge branch 'master' into faster_source_filter

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Fail early is double star excludes

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Turns out this is fixed in an unreleased version of jackson!

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Add better support for metric data types (TSDB) #74660

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benwtrent mentioned this pull request Oct 13, 2021

[ML] Optimize source extraction for categorize_text aggregation #79099

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benwtrent mentioned this pull request Oct 21, 2021

Optimize significant_text aggregation to only parse the field it requires from _source #79651

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nik9000 mentioned this pull request Dec 10, 2021

Xcontent filter: Add map and builder filter benchmark #81575

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mushao999 mentioned this pull request Dec 21, 2021

optimize source filtering in SourceFieldMapper #81970

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Memory efficient source filtering #25168

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Memory efficient xcontent filtering #77154

Memory efficient xcontent filtering #77154

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Memory efficient xcontent filtering #77154

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