Removed map of subquery to subquery index in favor of storing index as part of DISI wrapper to improve hybrid query latencies by 20%

[martin-gaievski]

In this PR we're continue to improve latency of hybrid query as part of meta issue https://github.com/opensearch-project/neural-search/issues/705.

Based on following flamegraph next area that may give high boost is a lookup of sub-query index by the query. That is needed to get index of sub-query and store score of that sub-query for one document (code ref).

Most time (~28%) is taken by store and lookup of the index based on query as a key. Depending on the exact sub-query calculation of its hash code can be slow (hybrid query works with any type of OpenSearch query). That is a problem on large datasets as this is done for each doc by each sub-query.

We can avoid creation and usage of that query to index map by storing sub-query index at time we create collection of DISIWrapers.

As per benchmark results that gives about 20% performance boost. I've run it on 2.13 using noaa OSB workload, all times are in ms:

Before the change (baseline)

One sub-query that selects 11M documents

Bool: p50 88.2863 | p90 103.777
Hybrid: p50 299.427 | p90 319.797

One sub-query that selects 1.6K documents

Bool: p50 92.7222 | p90 98.6847
Hybrid: p50 94.5511 | p90 111.645

Three sub-query that select 15M documents

Bool: p50 98.0301 | p90 108.948
Hybrid: p50 475.319 | p90 515.999

After the change:

One sub-query that selects 11M documents

Bool: p50 97.9306 | p90 116.299
Hybrid: p50 228.696 | p90 249.665

One sub-query that selects 1.6K documents

Bool: p50 87.3152 | p90 89.3061
Hybrid: p50 89.9654 | p90 92.349

Three sub-query that select 15M documents

Bool: p50 97.9891 | p90 114.396
Hybrid: p50 353.631 | p90 377.527

Issues Resolved

https://github.com/opensearch-project/neural-search/issues/705

Check List

• [X] New functionality includes testing.
  • [X] All tests pass
• [X] Commits are signed as per the DCO using --signoff

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