Key | Default | Type | Description
--- | --- | --- | ---
##### table.optimizer.skewed-join-optimization.strategy Batch | auto | Enum | Flink will handle skew in shuffled joins (sort-merge and hash) at runtime by splitting data according to the skewed join key. The value of this configuration determines how Flink performs this optimization. AUTO means Flink will automatically apply this optimization, FORCED means Flink will enforce this optimization even if it introduces extra hash shuffle, and NONE means this optimization will not be executed. Possible values: "auto": Flink will automatically perform this optimization. "forced": Flink will perform this optimization even if it introduces extra hash shuffling. "none": Skewed join optimization will not be performed.
##### table.optimizer.skewed-join-optimization.skewed-factor Batch | 4.0 | Double | When a join operator instance encounters input data that exceeds N times the median size of other concurrent join operator instances, it is considered skewed (where N represents this skewed-factor). In such cases, Flink may automatically split the skewed data into multiple parts to ensure a more balanced data distribution, unless the data volume is below the skewed threshold(defined using table.optimizer.skewed-join-optimization.skewed-threshold).
##### table.optimizer.skewed-join-optimization.skewed-threshold Batch | 256 mb | MemorySize | When a join operator instance encounters input data that exceeds N times the median size of other concurrent join operator instances, it is considered skewed (where N represents the table.optimizer.skewed-join-optimization.skewed-factor). In such cases, Flink may automatically split the skewed data into multiple parts to ensure a more balanced data distribution, unless the data volume is below this skewed threshold.
##### table.optimizer.adaptive-broadcast-join.strategy Batch | auto | Enum | Flink will perform broadcast hash join optimization when the runtime statistics on one side of a join operator is less than the threshold `table.optimizer.join.broadcast-threshold`. The value of this configuration option decides when Flink should perform this optimization. AUTO means Flink will automatically choose the timing for optimization, RUNTIME_ONLY means broadcast hash join optimization is only performed at runtime, and NONE means the optimization is only carried out at compile time. Possible values: "auto": Flink will automatically choose the timing for optimization "runtime_only": Broadcast hash join optimization is only performed at runtime. "none": Broadcast hash join optimization is only carried out at compile time.
##### table.optimizer.agg-phase-strategy Batch Streaming | AUTO | Enum | Strategy for aggregate phase. Only AUTO, TWO_PHASE or ONE_PHASE can be set. AUTO: No special enforcer for aggregate stage. Whether to choose two stage aggregate or one stage aggregate depends on cost. TWO_PHASE: Enforce to use two stage aggregate which has localAggregate and globalAggregate. Note that if aggregate call does not support optimize into two phase, we will still use one stage aggregate. ONE_PHASE: Enforce to use one stage aggregate which only has CompleteGlobalAggregate. Possible values: "AUTO" "ONE_PHASE" "TWO_PHASE"
##### table.optimizer.bushy-join-reorder-threshold Batch Streaming | 12 | Integer | The maximum number of joined nodes allowed in the bushy join reorder algorithm, otherwise the left-deep join reorder algorithm will be used. The search space of bushy join reorder algorithm will increase with the increase of this threshold value, so this threshold is not recommended to be set too large. The default value is 12.
##### table.optimizer.distinct-agg.split.bucket-num Streaming | 1024 | Integer | Configure the number of buckets when splitting distinct aggregation. The number is used in the first level aggregation to calculate a bucket key 'hash_code(distinct_key) % BUCKET_NUM' which is used as an additional group key after splitting.
##### table.optimizer.distinct-agg.split.enabled Streaming | false | Boolean | Tells the optimizer whether to split distinct aggregation (e.g. COUNT(DISTINCT col), SUM(DISTINCT col)) into two level. The first aggregation is shuffled by an additional key which is calculated using the hashcode of distinct_key and number of buckets. This optimization is very useful when there is data skew in distinct aggregation and gives the ability to scale-up the job. Default is false.
##### table.optimizer.dynamic-filtering.enabled Batch Streaming | true | Boolean | When it is true, the optimizer will try to push dynamic filtering into scan table source, the irrelevant partitions or input data will be filtered to reduce scan I/O in runtime.
##### table.optimizer.incremental-agg-enabled Streaming | true | Boolean | When both local aggregation and distinct aggregation splitting are enabled, a distinct aggregation will be optimized into four aggregations, i.e., local-agg1, global-agg1, local-agg2, and global-agg2. We can combine global-agg1 and local-agg2 into a single operator (we call it incremental agg because it receives incremental accumulators and outputs incremental results). In this way, we can reduce some state overhead and resources. Default is enabled.
##### table.optimizer.join-reorder-enabled Batch Streaming | false | Boolean | Enables join reorder in optimizer. Default is disabled.
##### table.optimizer.join.broadcast-threshold Batch | 1048576 | Long | Configures the maximum size in bytes for a table that will be broadcast to all worker nodes when performing a join. By setting this value to -1 to disable broadcasting.
##### table.optimizer.multiple-input-enabled Batch | true | Boolean | When it is true, the optimizer will merge the operators with pipelined shuffling into a multiple input operator to reduce shuffling and improve performance. Default value is true.
##### table.optimizer.non-deterministic-update.strategy Streaming | IGNORE | Enum | When it is `TRY_RESOLVE`, the optimizer tries to resolve the correctness issue caused by 'Non-Deterministic Updates' (NDU) in a changelog pipeline. Changelog may contain kinds of message types: Insert (I), Delete (D), Update_Before (UB), Update_After (UA). There's no NDU problem in an insert only changelog pipeline. For updates, there are three main NDU problems: 1. Non-deterministic functions, include scalar, table, aggregate functions, both builtin and custom ones. 2. LookupJoin on an evolving source 3. Cdc-source carries metadata fields which are system columns, not belongs to the entity data itself. For the first step, the optimizer automatically enables the materialization for No.2(LookupJoin) if needed, and gives the detailed error message for No.1(Non-deterministic functions) and No.3(Cdc-source with metadata) which is relatively easier to solve by changing the SQL. Default value is `IGNORE`, the optimizer does no changes. Possible values: "TRY_RESOLVE" "IGNORE"
##### table.optimizer.reuse-optimize-block-with-digest-enabled Batch Streaming | false | Boolean | When true, the optimizer will try to find out duplicated sub-plans by digest to build optimize blocks (a.k.a. common sub-graphs). Each optimize block will be optimized independently.
##### table.optimizer.reuse-source-enabled Batch Streaming | true | Boolean | When it is true, the optimizer will try to find out duplicated table sources and reuse them. This works only when table.optimizer.reuse-sub-plan-enabled is true.
##### table.optimizer.reuse-sub-plan-enabled Batch Streaming | true | Boolean | When it is true, the optimizer will try to find out duplicated sub-plans and reuse them.
##### table.optimizer.runtime-filter.enabled Batch | false | Boolean | A flag to enable or disable the runtime filter. When it is true, the optimizer will try to inject a runtime filter for eligible join.
##### table.optimizer.runtime-filter.max-build-data-size Batch | 150 mb | MemorySize | Max data volume threshold of the runtime filter build side. Estimated data volume needs to be under this value to try to inject runtime filter.
##### table.optimizer.runtime-filter.min-filter-ratio Batch | 0.5 | Double | Min filter ratio threshold of the runtime filter. Estimated filter ratio needs to be over this value to try to inject runtime filter.
##### table.optimizer.runtime-filter.min-probe-data-size Batch | 10 gb | MemorySize | Min data volume threshold of the runtime filter probe side. Estimated data volume needs to be over this value to try to inject runtime filter.This value should be larger than `table.optimizer.runtime-filter.max-build-data-size`.
##### table.optimizer.source.report-statistics-enabled Batch Streaming | true | Boolean | When it is true, the optimizer will collect and use the statistics from source connectors if the source extends from SupportsStatisticReport and the statistics from catalog is UNKNOWN.Default value is true.
##### table.optimizer.sql2rel.project-merge.enabled Batch Streaming | false | Boolean | If set to true, it will merge projects when converting SqlNode to RelNode. Note: it is not recommended to turn on unless you are aware of possible side effects, such as causing the output of certain non-deterministic expressions to not meet expectations(see FLINK-20887).
##### table.optimizer.union-all-as-breakpoint-enabled Batch Streaming | true | Boolean | When true, the optimizer will breakup the graph at union-all node when it's a breakpoint. When false, the optimizer will skip the union-all node even it's a breakpoint, and will try find the breakpoint in its inputs.