From 88296363f06f77235cc0a16b8bb1bd6cf4d3cc05 Mon Sep 17 00:00:00 2001 From: duanmeng Date: Sun, 15 Dec 2024 13:03:06 +0800 Subject: [PATCH] add tracing blog --- .../blog/2024-12-15-tracing-query-tracing.mdx | 402 ++++++++++++++++++ website/blog/authors.yml | 18 + website/static/img/tracing.png | Bin 0 -> 261206 bytes 3 files changed, 420 insertions(+) create mode 100644 website/blog/2024-12-15-tracing-query-tracing.mdx create mode 100644 website/static/img/tracing.png diff --git a/website/blog/2024-12-15-tracing-query-tracing.mdx b/website/blog/2024-12-15-tracing-query-tracing.mdx new file mode 100644 index 000000000000..cecdc351da22 --- /dev/null +++ b/website/blog/2024-12-15-tracing-query-tracing.mdx @@ -0,0 +1,402 @@ +--- +slug: velox-query-tracing +title: "Velox Query Tracing" +authors: [duanmeng, xiaoxmeng, tanjialiang] +tags: [tech-blog, tracing] +--- + +## Background + +The query trace tool helps analyze and debug query performance and correctness issues. It helps prevent +interference from external noise in a production environment (such as storage, network, etc.) by allowing +replay of a part of the query plan and dataset in an isolated environment, such as a local machine. +This is much more efficient for query performance analysis and issue debugging, as it eliminates the need +to replay the whole query in a production environment. + +## How Tracing Works + +The tracing process consists of two distinct phases: the tracing phase and the replaying phase. The +tracing phase is executed within a production environment, while the replaying phase is conducted in +a local development environment. + +**Tracing Phase** +1. Trace replay required metadata, including the query plan fragment, query configuration, +and connector properties, is recorded during the query task initiation. +2. Throughout query processing, each traced operator logs the input vectors or splits +storing them in a designated storage location. +3. The metadata and splits are serialized in JSON format, and the operator data inputs are +serialized using a [presto serializer](https://prestodb.io/docs/current/develop/serialized-page.html). + +**Replaying Phase** +1. Read and deserialize the recorded query plan, extract the traced plan node, and assemble a plan +fragment with customized source and sink nodes. +2. The source node reads the input from the serialized operator inputs on storage and the sink operator +prints or logs out the execution stats. +3. Build a task with the assembled plan fragment in step 1. Apply the recorded query configuration and +connector properties to replay the task with the same input and configuration setup as in production. + +**NOTE**: The presto serialization might lose input vector encoding, such as lazy vector and nested dictionary +encoding, which affects the operator’s execution. Hence, it might not always be the same as in production. + +
+ +
+ +## Tracing Framework + +The tracing framework consists of three components: +1. **Query Trace Writers**: records the metadata, input data, and scan splits. +2. **Query Trace Readers**: load the metadata, read input data, and scan splits. +3. **Query Trace Replayers**: display query summaries or replay the execution of the target operator. + +### Trace Writers + +There are three types of writers: `TaskTraceMetadataWriter`, `OperatorTraceInputWriter`, +and `OperatorTraceSplitWriter`. They are used in the prod or shadow environment to record +the real execution data. + +**TaskTraceMetadataWriter** + +The `TaskTraceMetadataWriter` records the query metadata during task creation, serializes it, +and saves it into a file in JSON format. There are two types of metadata: +1. **Query Configurations and Connector Properties**: These are user-specified per query and can +be serialized as JSON map objects (key-value pairs). +2. **Task Plan Fragment** (aka Plan Node Tree): This can be serialized as a JSON object, a feature +already supported in Velox (see [#4614](https://github.com/facebookincubator/velox/issues/4614), +[#4301](https://github.com/facebookincubator/velox/issues/4301), and +[#4398](https://github.com/facebookincubator/velox/issues/4398)). + +The metadata is saved as a single JSON object string in the metadata file. It would look similar +to the following simplified, pretty-printed JSON string (with some content removed for brevity): + +```JSON +{ + "planNode":{ + "nullAware": false, + "outputType":{...}, + "leftKeys":[...], + "rightKeys":[...], + "joinType":"INNER", + "sources":[ + { + "outputType":{...}, + "tableHandle":{...}, + "assignments":[...], + "id":"0", + "name":"TableScanNode" + }, + { + "outputType":{...}, + "tableHandle":{...}, + "assignments":[...], + "id":"1", + "name":"TableScanNode" + } + ], + "id":"2", + "name":"HashJoinNode" + }, + "connectorProperties":{...}, + "queryConfig":{"query_trace_node_ids":"2", ...} +} +``` + +**OperatorTraceInputWriter** + +The `OperatorTraceInputWriter` records the input vectors from the target operator, it uses a Presto +serializer to serialize each vector batch and flush immediately to ensure that replay is possible +even if a crash occurs during execution. + +It is created during the target operator's initialization and writes data in the `Operator::addInput` +method during execution. It finishes when the target operator is closed. However, it can finish early +if the recorded data size exceeds the limit specified by the user. + +**OperatorTraceSplitWriter** + +The `OperatorTraceSplitWriter` captures the input splits from the target `TableScan` operator. It +serializes each split and immediately flushes it to ensure that replay is possible even if a crash +occurs during execution. + +Each split is serialized as follows: + +`| length : uint32_t | split : JSON string | crc32 : uint32_t |` + +### Storage Location + +It is recommended to store traced data in a remote storage system to ensure its preservation and +accessibility even if the computation clusters are reconfigured or encounter issues. This also +helps prevent nodes in the cluster from failing due to local disk exhaustion. + +Users should start by creating a root directory. Writers will then create subdirectories within +this root directory to organize the traced data. A well-designed directory structure will keep +the data organized and accessible for replay and analysis. + +**Metadata Location** + +The `TaskTraceMetadataWriter` is set up during the task creation so it creates a trace directory +named `$rootDir/$queryId/$taskId`. + +**Input Data and Split Location** + +The task generates Drivers and Operators, and each is identified by a set of IDs. Each driver +is assigned a pipeline ID and a driver ID. Pipeline IDs are sequential numbers starting from zero, +and driver IDs are also sequential numbers starting from zero but are scoped to a specific pipeline, +ensuring uniqueness within that pipeline. Additionally, each operator within a driver is assigned a +sequential operator ID, starting from zero and unique within the driver. + +The node ID consolidates the tracing for the same tracing plan node. The pipeline ID isolates the +tracing data between operators created from the same plan node (e.g., HashProbe and HashBuild from +the HashJoinNode). The driver ID isolates the tracing data of peer operators in the same pipeline +from different drivers. + +Correspondingly, to ensure the organized and isolated tracing data storage, the `OperatorTraceInputWriter` +and `OpeartorTraceSplitWriter` are set up during the operator initialization and create a data or split +tracing directory in + +```Shell +$rootDir/$queryId$taskId/$nodeId/$pipelineId/$driverId +```` + +The following is a typical `HashJoinNode` traced metadata and data storage directory structure: + +```SHEL +trace ---------------------------------------------------> rootDir +└── query-1 -------------------------------------------> query ID + └── task-1 ----------------------------------------> task ID + ├── 2 -----------------------------------------> node ID + │ ├── 0 -------------------------> pipeline ID (probe) + │ │ ├── 0 -------------------------> driver ID (0) + │ │ │ ├── op_input_trace.data + │ │ │ └── op_trace_summary.json + │ │ └── 1 -------------------------> driver ID (1) + │ │ ├── op_input_trace.data + │ │ └── op_trace_summary.json + │ └── 1 -------------------------> pipeline ID (build) + │ ├── 0 ---------------------------> driver ID (0) + │ │ ├── op_input_trace.data + │ │ └── op_trace_summary.json + │ └── 1 -------------------------> driver ID (1) + │ ├── op_input_trace.data + │ └── op_trace_summary.json + └── task_trace_meta.json ----------------> query metadata + +``` + +### Memory Management + +Add a new leaf system pool named tracePool for tracing memory usage, and expose it +like `memory::MemoryManager::getInstance()->tracePool()`. + +### Trace Readers + +Three types of readers correspond to the query trace writers: `TaskTraceMetadataReader`, +`OperatorTraceInputReader`, and `OperatorTraceSplitReader`. The replayers typically use +them in the local environment, which will be described in detail in the Query Trace Replayer section. + +**TaskTraceMetadataReader** + +The `TaskTraceMetadataReader` can load the query metadata JSON file and extract the query +configurations, connector properties, and a plan fragment. The replayer uses these to build +a replay task. + +**OperatorTraceInputReader** + +The `OperatorTraceInputReader` reads and deserializes the input vectors in a tracing data file. +It is created and used by a `QueryTraceScan` operator which will be described in detail in +the **Query Trace Scan** section. + +**OperatorTraceSplitReader** + +The `OperatorTraceSplitReader` reads and deserializes the input splits in tracing split info files, +and produces a list of `exec::Split` for the query replay. + +### Trace Scan + +As outlined in the **How Tracing Works** section, replaying a non-leaf operator requires a +specialized source operator. This operator is responsible for reading data records during the +tracing phase and integrating with Velox’s `LocalPlanner` with a customized plan node and +operator translator. + +**TraceScanNode** + +We introduce a customized ‘TraceScanNode’ to replay a non-leaf operator. This node acts as +the source node and creates a specialized scan operator, known as `OperatorTraceScan` with +one per driver during the replay. The `TraceScanNode` contains the trace directory for the +designated trace node, the pipeline ID associated with it, and a driver ID list passed during +the replaying by users so that the OperatorTraceScan can locate the right trace input data or +split directory. + +**OperatorTraceScan** + + +As described in the **Storage Location** section, a plan node may be split into multiple pipelines, +each pipeline can be divided into multiple operators. Each operator corresponds to a driver, which +is a thread of execution. There may be multiple tracing data files for a single plan node, one file +per driver. + + +To identify the correct input data file associated with a specific `OperatorTraceScan` operator, it +leverages the trace node directory, pipeline ID, and driver ID list supplied by the TraceScanNode. + + +During the replay process, it uses its own driver ID as an index to extract the replay driver ID from +the driver ID list in the `TraceScanNode`. Along with the trace node directory and pipeline ID from +the `TraceScanNode`, it locates its corresponding input data file. + + +Correspondingly, an `OperatorTraceScan` operator uses a trace data file in +`$rootDir/$queryId/$taskId/$nodeId/$pipelineId/$dirverId` to create an +`OperatorTraceReader`. And `OperatorTraceScan::getOutput` method returns the vectors read by +its `OperatorTraceInputReader`, which returns the vectors in the same sequence order as originally +processed in the production execution. This ensures that the replaying maintains the same data flow +as in the original production execution. + +### Query Trace Replayer + +The query trace replayer is typically used in the local environment and works as follows: +1. Use `TaskTraceMetadataReader` to load traced query configurations, connector properties, +and a plan fragment. +2. Extract the target plan node from the plan fragment using the specified plan node ID. +3. Use the target plan node in step 2 to create a replay plan node. Create a replay plan +using `exec::test::PlanBuilder`. +4. If the target plan node is a `TableScanNode` + - Add the replay plan node to the replay plan as the source node. + - Get all the traced splits using `OperatorInputSplitReader`. + - Use the splits as inputs for task replaying. +5. For a non-leaf operator, add a `QueryTraceScanNode` as the source node to the replay plan and +then add the replay plan node. +6. Use `exec::test::AssertQueryBuilder` to add the sink node, apply the query +configurations (disable tracing), and connector properties, and execute the replay plan. + +The `OperatorReplayBase` provides the core functionality required for replaying an operator. +It handles the retrieval of metadata, creation of the replay plan, and execution of the plan. +Concrete operator replayers, such as `HashJoinReplayer` and `AggregationReplayer`, extend this +base class and override the `createPlanNode` method to create the specific plan node. + +## Query Trace Tool Usage + +### How To Enable Tracing + +Here is the list of query configs to enable tracing in the first phase: + +|Property Name|Default Value|Description| +|:-|:-:|:-| +|query_trace_enabled|false|Enable query tracing flag.| +|query_trace_dir|""|The root directory of a query to store tracing data.| +|query_trace_task_reg_exp|""|The regexp of traced task id. We only enable trace on a task if its ID matches.| +|query_trace_node_ids|""|A comma-separated list of plan node IDs whose input data will be traced. Empty string if only want to trace the query metadata.| +|query_trace_max_bytes|0|The max trace bytes limit. Tracing is disabled if zero.| + +### How To Replay + +After the traced query finishes, its metadata and the input data for the target tasks and operators +are all saved in the directory specified by `query_trace_dir`. + +To get a glance at the traced task, we can execute the following command: + +```SHELL +velox_query_replayer --root_dir /trace_root --task_id task-4 --summary +``` + +It would show something as follows: + +```SHELL +++++++Query trace summary++++++ +Number of tasks: 1 + +++++++Query configs++++++ + query_trace_task_reg_exp: .* + query_trace_node_ids: 2 + query_trace_max_bytes: 107374182400 + query_trace_dir: /tmp/velox_test_aJqeFd/basic/traceRoot/ + query_trace_enabled: 1 + +++++++Connector configs++++++ + +++++++Task query plan++++++ +-- HashJoin[2][INNER t0=u0] -> t0:BIGINT, t1:VARCHAR, t2:SMALLINT, t3:REAL, u0:BIGINT, u1:INTEGER, u2:SMALLINT, u3:VARCHAR + -- TableScan[0][table: hive_table] -> t0:BIGINT, t1:VARCHAR, t2:SMALLINT, t3:REAL + -- TableScan[1][table: hive_table] -> u0:BIGINT, u1:INTEGER, u2:SMALLINT, u3:VARCHAR + +++++++Task Summaries++++++ + +++++++Task task-1++++++ + +++++++Pipeline 2++++++ +driver 0: opType HashProbe, inputRows 70720, peakMemory 108.00KB +driver 1: opType HashProbe, inputRows 70720, peakMemory 108.00KB + +++++++Pipeline 3++++++ +driver 0: opType HashBuild, inputRows 48000, peakMemory 4.51MB +driver 1: opType HashBuild, inputRows 48000, peakMemory 2.25MB +``` + +Then we can execute an assembled query plan with the traced plan node using the following command in +the terminal or use the same gflags in your IDE to debug. + +```SHELL +velox_query_replayer --root_dir /Users/bytedance/work/native/trace --query_id query-1 --task_id task-1 --node_id 2 +``` + +```SHELL +Stats of replaying operator HashBuild : Output: 0 rows (0B, 0 batches), Cpu time: 48.63us, Wall time: 65.22us, Blocked wall time: 24.08ms, Peak memory: 4.51MB, Memory allocations: 16, Threads: 2, CPU breakdown: B/I/O/F (23.79us/0ns/14.46us/10.38us) + +Stats of replaying operator HashProbe : Output: 13578240 rows (1.17GB, 13280 batches), Cpu time: 3.99s, Wall time: 4.01s, Blocked wall time: 98.58ms, Peak memory: 108.00KB, Memory allocations: 12534, Threads: 2, CPU breakdown: B/I/O/F (8.52ms/1.59s/2.39s/20.29us) + +Memory usage: TaskCursorQuery_0 usage 0B reserved 0B peak 10.00MB + task.test_cursor 1 usage 0B reserved 0B peak 10.00MB + node.2 usage 0B reserved 0B peak 0B + op.2.1.1.OperatorTraceScan usage 0B reserved 0B peak 0B + op.2.1.0.OperatorTraceScan usage 0B reserved 0B peak 0B + node.N/A usage 0B reserved 0B peak 0B + op.N/A.0.1.CallbackSink usage 0B reserved 0B peak 0B + op.N/A.0.0.CallbackSink usage 0B reserved 0B peak 0B + node.1 usage 0B reserved 0B peak 10.00MB + op.1.1.0.HashBuild usage 0B reserved 0B peak 4.51MB + op.1.0.1.HashProbe usage 0B reserved 0B peak 108.00KB + op.1.1.1.HashBuild usage 0B reserved 0B peak 2.25MB + op.1.0.0.HashProbe usage 0B reserved 0B peak 108.00KB + node.0 usage 0B reserved 0B peak 0B + op.0.0.1.OperatorTraceScan usage 0B reserved 0B peak 0B + op.0.0.0.OperatorTraceScan usage 0B reserved 0B peak 0B + +``` + +We can replay a subset of drivers from the traced operator. For instance, if the traced operator +has 16 drivers during the tracing phase, we can selectively replay a specific driver by passing +--driver_ids, for example, `--driver_ids 1,3,5`. + +Here is a full list of supported command line arguments: + +|Flag|Default Value|Description| +|:-|:-:|:-| +|root_dir|""|The root directory where the replayer is reading the traced data, must be set.| +|summary|false|Show the summary of the tracing including numbers of tasks and task IDs. It also prints the query metadata including query configs, connectors properties, and query plan in JSON format.| +|query_id|""|Specify the target query ID, it must be set.| +|task_id|""|Specify the target task ID, it must be set.| +|node_id|""|Specify the target node ID, it must be set.| +|driver_ids|""|Specify the target driver IDs to replay.| +|shuffle_serialization_format|0|Specify the shuffle serialization format.| +|table_writer_output_dir|""|Specify the output directory of TableWriter.| +|hiveConnectorExecutorHwMultiplier|2.0|Hardware multiplier for hive connector.| + +## Future Work + +- Add support for more operators +- Customize the replay task execution instead of AssertQueryBuilder +- Supports IcebergHiveConnector +- Add trace replay for an entire pipeline + +## References + +- https://github.com/facebookincubator/velox/issues/9668 +- https://facebookincubator.github.io/velox/develop/task.html +- https://github.com/prestodb/presto/blob/e80152c74a5d2453116db2d173bafdd0db748c86/presto-native-execution/presto_cpp/main/TaskManager.cpp#L379 +- https://github.com/prestodb/presto/blob/e80152c74a5d2453116db2d173bafdd0db748c86/presto-native-execution/presto_cpp/main/TaskManager.cpp#L58 +- https://github.com/prestodb/presto/blob/master/presto-native-execution/presto_cpp/main/TaskResource.cpp#L88 +- https://github.com/prestodb/presto/blob/master/presto-native-execution/presto_cpp/main/QueryContextManager.cpp#L153 +- https://github.com/prestodb/presto/blob/master/presto-native-execution/presto_cpp/main/TaskManager.cpp#L426 +- https://github.com/prestodb/presto/blob/master/presto-native-execution/presto_cpp/main/TaskManager.cpp#L460 +- https://github.com/facebookincubator/velox/blob/5c4903fe2c1f640094295141206f23fe9a54c218/velox/core/PlanNode.cpp#L2223 +- https://github.com/prestodb/presto/blob/master/presto-native-execution/presto_cpp/main/common/ConfigReader.h +- https://prestodb.io/docs/current/develop/serialized-page.html diff --git a/website/blog/authors.yml b/website/blog/authors.yml index 82ac28b151b3..1512c94e5cd5 100644 --- a/website/blog/authors.yml +++ b/website/blog/authors.yml @@ -59,3 +59,21 @@ xumingming: title: Software Engineer @ Alibaba url: https://github.com/xumingming image_url: https://github.com/xumingming.png + +duanmeng: + name: Meng Duan (macduan) + title: Software Engineer @ ByteDance + url: https://github.com/duanmeng + image_url: https://github.com/duanmeng.png + +xiaoxmeng: + name: Xiaoxuan Meng + title: Software Engineer @ Meta + url: https://github.com/xiaoxmeng + image_url: https://github.com/xiaoxmeng.png + +tanjialiang: + name: Jialiang Tan + title: Software Engineer @ Meta + url: https://github.com/tanjialiang + image_url: https://github.com/tanjialiang.png diff --git a/website/static/img/tracing.png b/website/static/img/tracing.png new file mode 100644 index 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