关于clickhouse:ClickHouse-源码泛读

ClickHouse 源码泛读

前言

首先从最整体的视角看下ClickHouse的解决流程：

入口函数
TCP/HTTP/RPCHandler::runImpl

• 构建pipeline
  state.io = executeQuery()
• 调度执行pipeline, reply to client

if(state.io.pipeline.pushing()) {
  processInsertQuery();
} else if (state.io.pipeline.pulling()) {
  processOrdinaryQueryWithProcessors();
} else if ... {
  ...
}

整体分为两大块：

• 解析sql，构建pipeline。
• 而后依据pipeline的特点（insert or other）抉择对应的调度器执行pipeline，拿到后果返回给客户端。
  对于第二局部能够参考我之前写的文章：ClickHouse之Pipeline执行引擎，这篇文章次要剖析第一局部。

executeQuery

地位：src/Interpreters/executeQuery.cpp 1073

转发到executeQueryImpl。

executeQueryImpl

地位：src/Interpreters/executeQuery.cpp 358

解析SQL，并依据sql类型结构对应的Interpreter，调用Interpreter的execute()函数，取得pipeline，本文以Select语句为例进行剖析。

InterpreterSelectQuery::execute

地位：src/Interpreters/InterpreterSelectQuery.cpp 684

• 结构QueryPlan
• 依据QueryPlan结构QueryPipelineBuilder
• 依据builder结构pipeline

其中第二局部和第三局部的逻辑比较简单，本文暂且略过不表，重点剖析第一局部。

InterpreterSelectQuery::buildQueryPlan

地位：src/Interpreters/InterpreterSelectQuery.cpp 656

次要工作转发到executeImpl

InterpreterSelectQuery::executeImpl

地位：src/Interpreters/InterpreterSelectQuery.cpp 1105

/// Read the data from Storage. from_stage - to what stage the request was completed in Storage.
executeFetchColumns(from_stage, query_plan);

/// 依据解析后的ast以及其余信息向query_plan中一直增加各种类型的QueryPlanStep，注：QueryPlan实际上是一个树状构造，树节点类型为QueryPlanStep。

这里将executeFetchColumns独自列出来，因为这里波及到构建从存储引擎读取数据的QueryPlanStep，本文着重剖析这里。

InterpreterSelectQuery::executeFetchColumns

地位：src/Interpreters/InterpreterSelectQuery.cpp 1926

函数前半部分设计很多优化相干以及各种参数的获取，在刚开始浏览源码的时候这些内容能够暂且跳过，首先梳理分明整个我的项目的枝干，由粗到细缓缓剖析，否则很容易迷失在繁冗的细节中。关注2159行这里：

storage->read(query_plan, required_columns, storage_snapshot, query_info, context, processing_stage, max_block_size, max_streams);

StorageMergeTree::read

地位：src/Storages/StorageMergeTree.cpp 215

关注这里：

if (auto plan = reader.read(column_names, storage_snapshot, query_info, local_context, max_block_size, num_streams, processed_stage, nullptr, enable_parallel_reading))
    query_plan = std::move(*plan);

MergeTreeDataSelectExecutor::read

地位：src/Storages/MergeTree/MergeTreeDataSelectExecutor.cpp 135

这里对于查问是否应用projection进行分状况解决，咱们暂且关注不应用projection的分支。

MergeTreeDataSelectExecutor::readFromParts

地位：src/Storages/MergeTree/MergeTreeDataSelectExecutor.cpp 1282

关注这部分代码：

    auto read_from_merge_tree = std::make_unique<ReadFromMergeTree>(
        std::move(parts),
        real_column_names,
        virt_column_names,
        data,
        query_info,
        storage_snapshot,
        context,
        max_block_size,
        num_streams,
        sample_factor_column_queried,
        max_block_numbers_to_read,
        log,
        merge_tree_select_result_ptr,
        enable_parallel_reading
    );

    QueryPlanPtr plan = std::make_unique<QueryPlan>();
    plan->addStep(std::move(read_from_merge_tree));
    return plan;

剖析到这里可知，在结构QueryPlan阶段咱们实际上只往QueryPlan中增加了一个QueryPlanStep，它的类型是ReadFromMergeTree，读者能够看下这个类的继承关系验证，它的确是QueryPlanStep子类型。接下来的重点就是剖析ReadFromMergeTree这个类型。

在剖析之前咱们有必要晓得以下信息：
在 依据QueryPlan结构QueryPipelineBuilder阶段，咱们实际上依赖于QueryPlanStep的虚函数：

/// Add processors from current step to QueryPipeline.
/// Calling this method, we assume and don't check that:
///   * pipelines.size() == getInputStreams.size()
///   * header from each pipeline is the same as header from corresponding input_streams
/// Result pipeline must contain any number of streams with compatible output header is hasOutputStream(),
///   or pipeline should be completed otherwise.
virtual QueryPipelineBuilderPtr updatePipeline(QueryPipelineBuilders pipelines, const BuildQueryPipelineSettings & settings) = 0;

然而咱们发现ReadFromMergeTree并没有重写这个函数，起因如下：
ReadFromMergeTree的继承链为 ReadFromMergeTree -> ISourceStep -> QueryPlanStep。
在ISourceStep中曾经重写了这个函数，因而咱们只须要关注initializePipeline这个虚函数即可。

QueryPipelineBuilderPtr ISourceStep::updatePipeline(QueryPipelineBuilders, const BuildQueryPipelineSettings & settings)
{
    auto pipeline = std::make_unique<QueryPipelineBuilder>();
    QueryPipelineProcessorsCollector collector(*pipeline, this);
    initializePipeline(*pipeline, settings);
    auto added_processors = collector.detachProcessors();
    processors.insert(processors.end(), added_processors.begin(), added_processors.end());
    return pipeline;
}

ReadFromMergeTree::initializePipeline

关注：

auto result = getAnalysisResult();
...
pipe = spreadMarkRangesAmongStreams(
    std::move(result.parts_with_ranges),
    column_names_to_read);
...
pipeline.init(std::move(pipe));

能够看到，咱们是通过一个pipe初始化了pipeline（type : QueryPipelineBuilder），而后在ISourceStep::updatePipeline中返回并参加构建pipeline，因而咱们的重点转移到了如何构建这个pipe。注：对于QueryPipelineBuilder和Pipe的关系，大家能够跳转看看，其实只是一层很浅的封装。

ReadFromMergeTree::spreadMarkRangesAmongStreams

地位：src/Processors/QueryPlan/ReadFromMergeTree.cpp 375

转发到read函数

ReadFromMergeTree::read

地位：src/Processors/QueryPlan/ReadFromMergeTree.cpp 287
代码如下：

Pipe ReadFromMergeTree::read(
    RangesInDataParts parts_with_range, Names required_columns, ReadType read_type,
    size_t max_streams, size_t min_marks_for_concurrent_read, bool use_uncompressed_cache)
{
    if (read_type == ReadType::Default && max_streams > 1)
        return readFromPool(parts_with_range, required_columns, max_streams,
                            min_marks_for_concurrent_read, use_uncompressed_cache);

    auto pipe = readInOrder(parts_with_range, required_columns, read_type, use_uncompressed_cache, 0);

    /// Use ConcatProcessor to concat sources together.
    /// It is needed to read in parts order (and so in PK order) if single thread is used.
    if (read_type == ReadType::Default && pipe.numOutputPorts() > 1)
        pipe.addTransform(std::make_shared<ConcatProcessor>(pipe.getHeader(), pipe.numOutputPorts()));

    return pipe;
}

如果max_streams > 1，则转发到readFromPool，并且在pipe中增加一个ConcatProcessor，将多个source合并为一个。否则转发到readInOrder。

todo

整个零碎的链路切实太长了，前面的内容有工夫再剖析吧，之后的内容能够看下这篇文章。