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https://issues.apache.org/jira/browse/FLINK-36377?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=18013341#comment-18013341
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Yang Li commented on FLINK-36377:
---------------------------------
I've submitted a PR for this feature, while noticing that FLINK-38200 is
somewhat similar. The difference lies in the fact that FLINK-38200 did not
handle complex data types in aggregate functions. [~hackergin] PTAL
> Support the use of the LAST_VALUE aggregate function on ROW type data
> ----------------------------------------------------------------------
>
> Key: FLINK-36377
> URL: https://issues.apache.org/jira/browse/FLINK-36377
> Project: Flink
> Issue Type: Improvement
> Components: Runtime / State Backends
> Reporter: Yang Li
> Assignee: Yang Li
> Priority: Major
> Labels: pull-request-available
>
> h2. Introduction
> In Flink, after applying a group by, users may use LAST_VALUE to process
> certain fields to ensure that all fields have corresponding aggregation
> functions. Currently, LAST_VALUE does not support the ROW type syntax, so
> users always apply the LAST_VALUE function to each individual field
> separately, as shown below.
> _SELECT_
> _LAST_VALUE(bool_a) AS last_bool_a,_
> _LAST_VALUE(int2_b) AS last_int2_b,_
> _LAST_VALUE(int4_c) AS last_int4_c,_
> _LAST_VALUE(int8_d) AS last_int8_d,_
> _LAST_VALUE(float4_e) AS last_float4_e,_
> _LAST_VALUE(float4_f) AS last_float4_f,_
> _LAST_VALUE(numeric_g) AS last_numeric_g,_
> _LAST_VALUE(text_m) AS last_text_m,_
> _LAST_VALUE(varchar_p) AS last_varchar_p,_
> _date_h_
> _FROM source_table_
> _GROUP BY date_h_
>
> If the upstream operator is a retract stream, this approach will lead to
> redundant StateMap traversal. To facilitate retraction, Flink's internal
> {{LastValueWithRetractAggFunction}} will store all historical data related to
> the primary key. When the last value is deleted, it will traverse all keys in
> the orderToValue (which maps timestamps to data) and this {{MapView}} is
> stored in the form of {{{}StateMap{}}}. More {{LAST_VALUE}} functions leads
> to more times the read and write operations of RocksDB. Therefore, I advocate
> for handling {{ROW}} types with {{{}LAST_VALUE{}}}, allowing support for all
> fields with just one {{LAST_VALUE}} function as below.
> _SELECT_
> _LAST_VALUE(_
> _ROW(_
> _bool_a,_
> _int2_b,_
> _int4_c,_
> _int8_d,_
> _float4_e,_
> _float4_f,_
> _numeric_g,_
> _text_m,_
> _varchar_p_
> _)_
> _) AS row_data,_
> _date_h_
> _FROM source_table_
> _GROUP BY date_h_
> The experiment indicates that applying the {{ROW}} type to the {{LAST_VALUE}}
> function can improve the processing speed for retract streams, but has no
> effect on append-only streams.
> h2. Evaluation：
> The throughput of jobs was compared based on whether the {{ROW}} type was
> used in the {{LAST_VALUE}} function, considering both retract and append-only
> scenarios.
> h3. Retraction
> Use a deduplication operator to convert the append-only stream generated by
> datagen into a retract stream. Two jobs show difference in throughput (Row
> 4817: Mean 1808). Through flame graph analysis, applying the ROW type to the
> LAST_VALUE function reduces the consumption of the aggregate function calls
> to accumulate, with CPU usage for accumulate being (ROW 20.02%: Separated
> 66.98%). LastValueWithRetractAccumulator uses MapState storage MapView.
> Therefore, updating the LastValueWithRetractAccumulator requires reading from
> or writing to RocksDB.
> h3. AppendOnly
> Two jobs show little difference in throughput (Row 13411: Mean 10673).
> Further examination of the flame graphs for both processes reveals that the
> bottleneck
> for both jobs lies in getting {{RocksDBValueState}} which is called by
> {{{}GroupFunction{}}}. Using {{ROW}} aggregation does not yield significant
> optimization in this part. I suspect it's because Flink uses RowData to store
> data from multiple Accumulators, and every time the {{accState}} invokes the
> {{value}} method, it reads all the Accumulators at the same time. Therefore,
> the use of ROW optimization might not be very effective.
> h2. Conclusion
> # Using ROW type for LAST_VALUE Aggregation can improve the processing speed
> for retract streams, with effectiveness proportional to the number of fields
> contained in the {{{}ROW{}}}.
> # Using ROW type for LAST_VALUE Aggregation results in limited improvements
> , as the optimization effect on state backend read speed is not significant.
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