ESQL: Speed loading stored fields (#127348)

[nik9000]

This speeds up loading from stored fields by opting more blocks into the "sequential" strategy. This really kicks in when loading stored fields like text. And when you need less than 100% of documents, but more than, say, 10%. This is most useful when you need 99.9% of field documents. That sort of thing. Here's the perf numbers:

%100.0 {"took": 403 -> 401,"documents_found":1000000}
%099.9 {"took":3990 -> 436,"documents_found": 999000}
%099.0 {"took":4069 -> 440,"documents_found": 990000}
%090.0 {"took":3468 -> 421,"documents_found": 900000}
%030.0 {"took":1213 -> 152,"documents_found": 300000}
%020.0 {"took": 766 -> 104,"documents_found": 200000}
%010.0 {"took": 397 ->  55,"documents_found": 100000}
%009.0 {"took": 352 -> 375,"documents_found":  90000}
%008.0 {"took": 304 -> 317,"documents_found":  80000}
%007.0 {"took": 273 -> 287,"documents_found":  70000}
%005.0 {"took": 199 -> 204,"documents_found":  50000}
%001.0 {"took":  46 ->  46,"documents_found":  10000}

Let's explain this with an example. First, jump to main and load a million documents:

rm -f /tmp/bulk
for a in {1..1000}; do
    echo '{"index":{}}' >> /tmp/bulk
    echo '{"text":"text '$(printf %04d $a)'"}' >> /tmp/bulk
done

curl -s -uelastic:password -HContent-Type:application/json -XDELETE localhost:9200/test
for a in {1..1000}; do
    echo -n $a:
    curl -s -uelastic:password -HContent-Type:application/json -XPOST localhost:9200/test/_bulk?pretty --data-binary @/tmp/bulk | grep errors
done
curl -s -uelastic:password -HContent-Type:application/json -XPOST localhost:9200/test/_forcemerge?max_num_segments=1
curl -s -uelastic:password -HContent-Type:application/json -XPOST localhost:9200/test/_refresh
echo

Now query them all. Run this a few times until it's stable:

echo -n "%100.0 "
curl -s -uelastic:password -HContent-Type:application/json -XPOST 'localhost:9200/_query?pretty' -d'{
    "query": "FROM test | STATS SUM(LENGTH(text))",
    "pragma": {
        "data_partitioning": "shard"
    }
}' | jq -c '{took, documents_found}'

Now fetch 99.9% of documents:

echo -n "%099.9 "
curl -s -uelastic:password -HContent-Type:application/json -XPOST 'localhost:9200/_query?pretty' -d'{
    "query": "FROM test | WHERE NOT text.keyword IN (\"text 0998\") | STATS SUM(LENGTH(text))",
    "pragma": {
        "data_partitioning": "shard"
    }
}' | jq -c '{took, documents_found}'

This should spit out something like:

%100.0 { "took":403,"documents_found":1000000}
%099.9 {"took":4098, "documents_found":999000}

We're loading fewer documents but it's slower! What in the world?! If you dig into the profile you'll see that it's value loading:

$ curl -s -uelastic:password -HContent-Type:application/json -XPOST 'localhost:9200/_query?pretty' -d'{
    "query": "FROM test | STATS SUM(LENGTH(text))",
    "pragma": {
        "data_partitioning": "shard"
    },
    "profile": true
}' | jq '.profile.drivers[].operators[] | select(.operator | contains("ValuesSourceReaderOperator"))'
{
  "operator": "ValuesSourceReaderOperator[fields = [text]]",
  "status": {
    "readers_built": {
      "stored_fields[requires_source:true, fields:0, sequential: true]": 222,
      "text:column_at_a_time:null": 222,
      "text:row_stride:BlockSourceReader.Bytes": 1
    },
    "values_loaded": 1000000,
    "process_nanos": 370687157,
    "pages_processed": 222,
    "rows_received": 1000000,
    "rows_emitted": 1000000
  }
}
$ curl -s -uelastic:password -HContent-Type:application/json -XPOST 'localhost:9200/_query?pretty' -d'{
    "query": "FROM test | WHERE NOT text.keyword IN (\"text 0998\") | STATS SUM(LENGTH(text))",
    "pragma": {
        "data_partitioning": "shard"
    },
    "profile": true
}' | jq '.profile.drivers[].operators[] | select(.operator | contains("ValuesSourceReaderOperator"))'
{
  "operator": "ValuesSourceReaderOperator[fields = [text]]",
  "status": {
    "readers_built": {
      "stored_fields[requires_source:true, fields:0, sequential: false]": 222,
      "text:column_at_a_time:null": 222,
      "text:row_stride:BlockSourceReader.Bytes": 1
    },
    "values_loaded": 999000,
    "process_nanos": 3965803793,
    "pages_processed": 222,
    "rows_received": 999000,
    "rows_emitted": 999000
  }
}

It jumps from 370ms to almost four seconds! Loading fewer values! The second big difference is in the stored_fields marker. In the second on it's sequential: false and in the first sequential: true.

sequential: true uses Lucene's "merge" stored fields reader instead of the default one. It's much more optimized at decoding sequences of documents.

Previously we only enabled this reader when loading compact sequences of documents - when the entire block looks like

1, 2, 3, 4, 5, ... 1230, 1231

If there are any gaps we wouldn't enable it. That was a very conservative thing we did long ago without doing any experiments. We knew it was faster without any gaps, but not otherwise. It turns out it's a lot faster in a lot more cases. I've measured it as faster for 99% gaps, at least on simple documents. I'm a bit worried that this is too aggressive, so I've set made it configurable and made the default being to use the "merge" loader with 10% gaps. So we'd use the merge loader with a block like:

1, 11, 21, 31, ..., 1231, 1241

Also backports ESQL: Fix test locale (#127566)

Was formatting a string and didn't include Locale.ROOT so sometimes the string would use the Arabic ٩ instead of 9. And JSON doesn't parse those.

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[dnhatn]

LGTM. Thank you!