Randomly pick start idx of test dataset in concurrency search.

[Sheharyar570]

In the current implementation of concurrent search, all threads begin querying the test dataset starting at index 0 and proceed sequentially through the dataset (looping multiple times). As a consequence, all threads are making the same query to the database almost at the same time. To make it more realistic the PR starts each query from a different starting index, resulting it different threads running different queries.
Databases like Postgres, rely on storing the data/index on disk and loading it on demand in memory. In case of constraint memory, where index doesn’t fit in memory, the first query typically hits the table, but subsequent queries are served from the index cached in shared_buffers. This skews performance metrics by making it difficult to observe the true cost of querying uncached data.
Note that there is no ramp-up or pre-warming step in VectorDBBench. However, there is a serial search task during which recall is calculated, where the set of 1000 queries is executed (this is the same set that is queried during the conc search). Therefore, the relevant index should already be present in memory, if enough memory is there. However, in case memory is not large enough to hold the index, it makes sense that randomizing the idx will result it more realistic numbers.

In summary, if index fits in memory, then this and previous approach would give same QPS. However in case index > memory, QPS numbers with these changes will decrease as different threads are executing different queries at the same time and to accommodate the index in memory certain data needs to be evicted.

Tested on:
Database: Postgres
Algo: pgvectorhnsw

[Sheharyar570]

/assign @XuanYang-cn

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This pull-request has been approved by: alwayslove2013, Sheharyar570
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[alwayslove2013]

@Sheharyar570 Well done! Randomizing the start_idx can reduce cache hits, which will make the VectorDBBench tests more representative of real-world scenarios. You did an amazing job!