wjhypo opened a new pull request #11307:
URL: https://github.com/apache/druid/pull/11307


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   Fixes #11301
   
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   ### Description
   
   
   
   
   
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a corresponding issue (referenced above), it's not necessary to repeat the 
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   - Choice of algorithms
   
   If `enableInMemoryBitmap` is set to true in `tuningConfig` in the supervisor 
spec, a bitmap is maintained during the data is in incremental index for each 
dimension that uses bitmap in the schema, i.e., for a dimension, only if bitmap 
is to be created when creating the immutable segment during handoff, will a 
corresponding temporary in memory bitmap be created.
   
   This in-memory bitmap is not serialized or reused during persistence to 
completely separate from the other bitmap created in the immutable segment for 
clean logic separation.
   
   In terms of the implementation of this in-memory bitmap, currently user 
can't specify it anywhere and only Roaring bitmap is used as the default 
implementation. The PR doesn't add choices to pick other compressed bitmaps 
like Concise Bitmap because we don't see clear performance benefits to add 
other implementations at the moment.
   
   In terms of the implementation of Roaring bitmap, there are 3 
implementations available in the Roaring bitmap library: 
`MutableRoaringBitmap`, `ImmutableRoaringBitmap` and `RoaringBitmap`. 
`RoaringBitmap` is supposed to be more performant than the other two in this 
case because it doesn't need to maintain a byte buffer backend and byte buffer 
backend is only convenient if we need to do Mmap or if there's serde involved, 
which is the case for batch ingested immutable segments querying. However, the 
implementation in the PR tries reusing the logic in the current Java classes 
built for the batch ingested immutable segments, so the byte buffer backed 
implementation `MutableRoaringBitmap` is used to store row indexes for each new 
rows. During query time, a snapshot of the bitmap is needed so 
`MutableRoaringBitmap` is cast to `ImmutableRoaringBitmap`, which involves 
copying at one of the stages of the conversion. During the copy, for high 
cardinality dimensions, the number of row indexe
 s for a given row value should be small so the copying overhead should be 
minimal; for low cardinality dimensions, supposedly there are more data to copy 
than the previous case, though we don't see obvious issue during testing and we 
can add metrics on the copying time and as long as the benefit outweigh the no 
bitmap full scan case, it should still be worthwhile.
   
    - Behavioral aspects. What configuration values are acceptable? How are 
corner cases and error conditions handled, such as when there are insufficient 
resources?
   
   Enable in memory bitmap construction in supervisor spec
   ```
   {
     "type": "kafka",
     "tuningConfig": {
       ...
       "enableInMemoryBitmap": true
       ...
     }
   }
   ```
   
    - Class organization and design (how the logic is split between classes, 
inheritance, composition, design patterns)
   
    The implementation tries piggybacking on the bitmap query related classes 
of batch generated immutable segment index (QueryableIndex and its support 
classes) as much as possible to reduce duplicate work.
   
   ### Benchmark
   
   #### Production Cluster Benchmark
   
   <img width="631" alt="Screen Shot 2021-08-24 at 5 10 07 PM" 
src="https://user-images.githubusercontent.com/8614743/130705911-fb6d749b-07f2-400b-ac5d-419209f164c4.png";>
   Figure 1: Production cluster use case 1 P99 latency reduction on Middle 
Managers. The diagram shows Middle Manager P99 latency reduction (more than 
30x) with the same middle manager capacity before and after enabling in memory 
bitmap for use case 1.
   
   <img width="446" alt="Screen Shot 2021-08-24 at 5 17 41 PM" 
src="https://user-images.githubusercontent.com/8614743/130706345-faaf17d6-4fa8-42c9-8941-40f05e96ef5a.png";>
   Figure 2: Production cluster use case 1 QPS on Middle Managers. The diagram 
shows the corresponding increase of QPS throughput before and after enabling 
in-memory bitmap for use case 1 in Figure 1.
   
   <img width="642" alt="Screen Shot 2021-08-24 at 5 12 10 PM" 
src="https://user-images.githubusercontent.com/8614743/130705930-6a133036-ccc6-45fa-8d4b-db2d9e06846f.png";>
   Figure 3: Production cluster use case 2 P90 latency (ms) and infrastructure 
cost reduction on Middle Managers. The diagram shows a 10x reduction in Middle 
Manager P90 latency before and after enabling in-memory bitmap for use case 2. 
The diagram also shows a follow-up 68.89% host capacity reduction without 
impacting latency (the green to red line switch).
   
   #### Unit Test Benchmark
   
   Additional unit test benchmark result is also included in the comments of 
this PR: https://github.com/apache/druid/pull/11307#issuecomment-859209115
   
   ### Correctness Validation
   We added a query context flag that controls the broker to only return 
results from middle manager hosts (not included in the PR). We added a 
temporary query time flag `useInMemoryBitmapInQuery` that can control whether 
to use the in-memory bitmap index during query time even though the in-memory 
bitmap index is already created during ingestion (this flag is removed in the 
PR created because it doesn't serve any purpose other than validation during 
development). We stopped the Kafka producer temporarily and left some records 
in memory in the incremental index (~852,000 records, when reaching 1M, 
persistence will trigger) before persistence triggered to verify that the 
results are the same when `useInMemoryBitmapInQuery` is set to true or false.
   
   <img width="371" alt="Screen Shot 2021-08-24 at 5 29 39 PM" 
src="https://user-images.githubusercontent.com/8614743/130707097-084c4dbf-775c-490f-9e3e-a8bb7b91424a.png";>
   Figure 4: Number of events ingested on Middle Managers for verification data 
source. The diagram shows the number of events ingested dropped to 0 and 
remained 0 as we stopped the Kafka producer.
   
   <img width="356" alt="Screen Shot 2021-08-24 at 5 29 28 PM" 
src="https://user-images.githubusercontent.com/8614743/130707099-77a83632-568e-48aa-b1f2-c3e296f23ae3.png";>
   Figure 5: Number of events in incremental index on Middle Managers for 
verification data source. The diagram shows the number of events in incremental 
index remained a little over 800k and temporarily not qualified for persistence 
threshold as we stopped the Kafka producer.
   
   We ran the follow queries (some sensitive business related column name and 
filter value in the queries before are replaced with dummy value but they were 
issued with real value during the verification).
   
   ```
   Query 1: aggregate sanity check. This query doesn't use in-memory bitmap in 
reality because no filter is there.
   SELECT __time, SUM(spend)/1000000.00 as spend,
   sum(paid_events) as paid_events,
   sum(cnt) as cnt, 
   sum(cnt_gross) as cnt_gross
   FROM verification_data_source
   WHERE __time >= timestamp'2021-07-20 20:00:00' 
   GROUP BY __time
   ORDER BY __time
   
   Query 2: filter with random value. Column `advertiserid` has in-memory 
bitmap index created.
   SELECT __time, SUM(spend)/1000000.00 as spend, sum(paid_events) as 
paid_events, sum(cnt) as cnt, sum(cnt_gross) as cnt_gross FROM 
verification_data_source WHERE advertiserid = '123' and __time >= 
timestamp'2021-07-20 20:00:00' group by __time
   
   Query 3: filter on one of top 10 large advertisers. Column `advertiserid` 
and column `apptype` have in-memory bitmap index created.
   SELECT __time, SUM(spend)/1000000.00 as spend, sum(paid_events) as 
paid_events, sum(cnt) as cnt, sum(cnt_gross) as cnt_gross FROM 
verification_data_source WHERE advertiserid = '123' and apptype = 3 and __time 
>= timestamp'2021-07-20 20:00:00' GROUP BY __time ORDER BY __time
   
   Query 4: filter on one of top 10 small advertiser. Column `advertiserid` and 
column `apptype` have in-memory bitmap index created.
   SELECT __time, SUM(spend)/1000000.00 as spend, sum(paid_events) as 
paid_events, sum(cnt) as cnt, sum(cnt_gross) as cnt_gross FROM 
verification_data_source WHERE advertiserid = '123' and __time >= 
timestamp'2021-07-20 20:00:00' GROUP BY __time ORDER BY __time
   ```
   We verified that non empty results are return and they are the same whether 
`useInMemoryBitmapInQuery` is set to true or false for all the above queries.
   
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      - [ ] using the [concurrency 
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   - [x] been tested in a test Druid cluster.
   


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