. . . . . . . . . . . . . . . . . . . . . . 295 7.9 In SQLSever and PostgreSQL, why does the aggregation column without alias throw ex‐ ception? . . . . . . . . . . . . . . . . . . . . . . . . . . . extract the parsing context, which can include tables, options, ordering items, grouping items, aggregation functions, pagination information, query conditions and placeholders that may be revised. Query order_id ORDER BY user_id, order_item_id; Another situation of column derivation is using AVG aggregation function. In distributed situations, it is not right to calculate the average value with avg1

processing and real-time data analysis in the same system, which greatly saves the cost. • Data aggregation and secondary processing scenarios The application data of most companies are scattered in different Information functions Y Y Y Y JSON functions Experimental Experimental Experimental Experimental Aggregation functions Y Y Y Y Window functions Y Y Y Y Miscellaneous functions Y Y Y Y Operators Y Y Y Y the following SQL statement to query SQL statements with different execution plans. select count(distinct plan_digest) as count, digest, min(query) from cluster_slow_query group by digest having count

processing and real-time data analysis in the same system, which greatly saves the cost. • Data aggregation and secondary processing scenarios The application data of most companies are scattered in different MICROSECOND(), MINUTE(), SEC- OND(), SYSDATE() * Type conversion function: CAST(time, real) * Aggregation functions: GROUP_CONCAT(), SUM(enum) – Support 512-bit SIMD – Enhance the cleanup algorithm for The wrong execution plan is caused by the shallow copy of schema columns when pushing down the aggregation operators on partitioned tables #27797 #26554 – Fix the issue that plan cache cannot detect changes

processing and real-time data analysis in the same system, which greatly saves the cost. • Data aggregation and secondary processing scenarios The application data of most companies are scattered in different INET_ATON(), INET6_ATON, INET6_NTOA() – Support Shuffled Hash Join calculation and Shuffled Hash Aggregation calculation in the MPP mode when a new collation is enabled – Optimize basic code to improve MPP example, {d '2020-01-01'}) cannot be used as the expression #25531 – Fix the issue that SELECT DISTINCT converted to Batch Get causes incorrect results #25320 – Fix the issue that backing off queries

necessary to wrap your query and apply a count to the rows of the inner query (such as when using DISTINCT or GROUP BY). Peewee will usually do this automatically, but in some cases you may need to manually retrieve scalar values by calling Query.scalar(). For instance: >>> PageView.select(fn.Count(fn.Distinct(PageView.url))).scalar() 100 You can retrieve multiple scalar values by passing as_tuple=True: Release 2.10.2 aggregate([name=None[, num_params=-1]]) Class-decorator for registering custom aggregation functions. Parameters • name – string name for the aggregate, defaults to the name of the class

processing and real-time data analysis in the same system, which greatly saves the cost. • Data aggregation and secondary processing scenarios The application data of most companies are scattered in different JSON functions Experimental Experimental Experimental Experimental Experimental Experimental Aggregation functions Y Y Y Y Y Y Window functions Y Y Y Y Y Y Miscellaneous functions Y Y Y Y Y Y Operators the following SQL statement to query SQL statements with different execution plans. select count(distinct plan_digest) as count, digest, min(query) from cluster_slow_query group by digest having count

transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary processing scenarios 41 TiDB is suitable for companies that need to aggregate scattered statements, aggregation by SQL cannot effectively identify issues. Starting from v8.4.0, you can choose to aggregate CPU time By TABLE or By DB. In 50 scenarios with multiple systems, the new aggregation method ing from v8.4.0, Index Join is sup- ported by default when the inner table has Selection �→ , Aggregation �→ , or Projection �→ opera- tors on it. 57 Variable name Change type Description tidb_ �→ opt_

transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary processing scenarios 36 TiDB is suitable for companies that need to aggregate scattered statements, aggregation by SQL cannot effectively identify issues. Starting from v8.4.0, you can choose to aggregate CPU time By TABLE or By DB. In 45 scenarios with multiple systems, the new aggregation method ing from v8.4.0, Index Join is sup- ported by default when the inner table has Selection �→ , Aggregation �→ , or Projection �→ opera- tors on it. 52 Variable name Change type Description tidb_ �→ opt_

transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary processing scenarios TiDB is suitable for companies that need to aggregate scattered Y Y Y Y Y Information functions Y Y Y Y Y Y Y Y Y Y Y JSON functions Y Y Y Y E E E E E E E Aggregation functions Y Y Y Y Y Y Y Y Y Y Y Window functions Y Y Y Y Y Y Y Y Y Y Y Miscellaneous functions analytical processing performance If your application involves complex analytical queries, such as aggregation and join operations, and these queries are performed on a large amount of data (more than 10 million

transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary processing scenarios TiDB is suitable for companies that need to aggregate scattered • TiFlash introduces HashAgg aggregation calculation modes to improve the perfor- mance for high NDV data #9196 @guo-shaoge Before v8.3.0, TiFlash has low aggregation calculation efficiency during the HashAgg aggregation when handling data with high NDV (number of distinct values). Starting from v8.3.0, TiFlash introduces multiple HashAgg aggregation calculation 38 modes to improve the aggregation performance