Proposal: A new aggregate function execution framework

• Author(s): @zz-jason
• Last updated: 2018-07-01
• Discussion at: https://github.com/pingcap/tidb/pull/6852

Abstract

This proposal proposes a new execution framework for the aggregate functions, to improve the execution performance of aggregate functions.

Background

In release-2.0, the framework of aggregate functions is located in the “expression/aggregation” package. In this framework, all the aggregate functions implement the Aggregation interface. It uses the AggEvaluateContext to store partial result for all the aggregate functions with all kind of possible argument types. The DistinctChecker in the AggEvaluateContext uses a []byte as the key of map, which is used to de-duplicate the values in the same group. During the execution, the Update interface is called to update the partial result for every input record. It enumerates every possible state during the execution of every aggregate function, which introduces a lot of CPU branch predictions.

It’s easy to implement a new aggregate function under this framework. But it has some disadvantages as well:

• Update is called for every input record. The per record function call could bring a huge overhead, especially when the execution involves tens of thousands of input records.
• Update is called for every possible aggregate state, which also introduces a lot of CPU branch predictions. For example, function AVG behaves different when the state is Partial1 and Final, it has to make a switch statement to handle all the possible states.
• GetResult returns a types.Datum as the final result of a group. Currently, TiDB uses Chunk to store the records during the query execution. The returned Datum has to be converted to Chunk outside the aggregate framework, which introduces a lot of data conversions and object allocations.
• AggEvaluateContext is used to store every possible partial result and maps, which consumes more memory than what is actually needed. For example, the COUNT only need a int64 field to store the row count it concerns.
• distinctChecker is used to de-duplicate values, and uses []byte as key. The encoding and decoding operations on the input values also bring a lot of CPU overhead, which can be avoided by directly using the input value as the key of that map.

Proposal

In this PR: https://github.com/pingcap/tidb/pull/6852, a new framework was proposed. The new framework locates in the “executor/aggfuncs” package.

In the new execution framework, every aggregate function implements the AggFunc interface. It uses the PartialResult, which is actually a unsafe.Pointer, as the type of the partial result for every aggregate function. unsafe.Pointer allows the partial result to be any kind of data object.

The AggFunc interface contains the following functions:

• AllocPartialResult allocates a specific data structure to store the partial result, initializes it, converts it to PartialResult and returns it back. Aggregate operator implementation, for example, stream aggregate operator, should hold this allocated PartialResult for further operations involving the partial result such as ResetPartialResult, UpdatePartialResult, etc.
• ResetPartialResult resets the partial result to the original state for a specific aggregate function. It converts the input PartialResult to the specific data structure which stores the partial result and then resets every field to the proper original state.
• UpdatePartialResult updates the specific partial result for an aggregate function using the input rows which all belong to the same data group. It converts the PartialResult to the specific data structure which stores the partial result and then iterates on the input rows and updates that partial result according to the functionality and the state of the aggregate function.
• AppendFinalResult2Chunk finalizes the partial result and appends the final result directly to the input Chunk. Like other operations, it converts the input PartialResult to the specific data structure firstly, calculates the final result and appends that final result to the Chunk provided.
• MergePartialResult evaluates the final result using the input PartialResults. Suppose the input PartialResults names are dst and src respectively. It converts dst and src to the same data structure firstly, merges the partial results and store the result in dst.

The new framework uses the Build() function to build an executable aggregate function. Its input parameters are:

• aggFuncDesc: the aggregate function representation used by the planner layer.
• ordinal: the ordinal of the aggregate functions. This is also the ordinal of the output column in the Chunk of the corresponding aggregate operator.

The Build() function builds an executable aggregate function for a specific input argument type, aggregate state, etc. The more specific, the better.

Rationale

Advantages:

• The partial result under the new framework can be any type. Aggregate functions can allocate the memory according to the exact need without any wasting. The OOM risk can also be reduced when it is used in the hash aggregate operator.
• That the partial result can be any type also means the aggregate functions can use a map with the specific input type as keys. For example, use map[types.MyDecimal] to de-duplicate the input decimal values. In this way, the overhead on the encoding and decoding operations on the old framework can be reduced.
• UpdatePartialResult is called with a batch of input records. The overhead caused by the per record function call on the framework can be saved. Since all the execution operators are using Chunk to store the input rows, in which the data belonging to the same column is stored consecutively in the memory, the aggregate functions should be executed one by one, take full utilization of the CPU caches, reduce the cache misses, and improve the execution performance.
• For every state and every kind of input type, a specific aggregate function should be implemented to handle it. This means the CPU branch predictions on the aggregate state and input value types can reduce during the execution of UpdatePartialResult, utilize the CPU pipelines, and improve the execution speed.
• AppendFinalResult2Chunk directly finalizes the partial result to the chunk, without converting it to Datum and then converting the Datum back into Chunk. This saves a lot of object allocation, reduces the overhead of golang’s gc worker, and avoids the unnecessary value conversions between Datum and Chunk.

Disadvantages:

• An aggregate function need to be implemented for every possible state and input type. This could introduces a lot of development work. And it takes more coding work to add a new aggregate function.

Compatibility

For now, the new framework is only supported in the stream aggregate operator. If the Build() function returns a nil pointer, it rolls back to the old framework.

So this new framework can be tested during the development, and all the results should be the same with the old framework.

Implementation

• Implement the new framework, including the AggFunc interface and the Build() function. @zz-jason
• Implement the AVG function under the new framework. @zz-jason
• Implement other aggregate functions under the new framework. @XuHuaiyu
• Add more integration tests using the random query generator. @XuHuaiyu
• Add unit tests for the new framework. @XuHuaiyu
• Infer input parameter type for new aggregate functions when extracting a Project operator under the Aggregate operator. @XuHuaiyu
• Use the new framework in the hash aggregate operator. @XuHuaiyu

Open issues (if applicable)