Apache Hudi代表 Hadoop 实现大数据的对象存储的Upserts、Deletes 和 Incrementals

<!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> This page describes in detail how to run end to end tests on a hudi dataset that helps in improving our confidence in a release as well as perform large scale performance benchmarks. # Objectives 1. Test with different versions of core libraries and components such as `hdfs`, `parquet`, `spark`, `hive` and `avro`. 2. Generate different types of workloads across different dimensions such as `payload size`, `number of updates`, `number of inserts`, `number of partitions` 3. Perform multiple types of operations such as `insert`, `bulk_insert`, `upsert`, `compact`, `query` 4. Support custom post process actions and validations # High Level Design The Hudi test suite runs as a long running spark job. The suite is divided into the following high level components : ## Workload Generation This component does the work of generating the workload; `inserts`, `upserts` etc. ## Workload Scheduling Depending on the type of workload generated, data is either ingested into the target hudi dataset or the corresponding workload operation is executed. For example compaction does not necessarily need a workload to be generated/ingested but can require an execution. ## Other actions/operations The test suite supports different types of operations besides ingestion such as Hive Query execution, Clean action etc. # Usage instructions ## Entry class to the test suite ``` org.apache.hudi.integ.testsuite.HoodieTestSuiteJob.java - Entry Point of the hudi test suite job. This class wraps all the functionalities required to run a configurable integration suite. ``` ## Configurations required to run the job ``` org.apache.hudi.integ.testsuite.HoodieTestSuiteJob.HoodieTestSuiteConfig - Config class that drives the behavior of the integration test suite. This class extends from com.uber.hoodie.utilities.DeltaStreamerConfig. Look at link#HudiDeltaStreamer page to learn about all the available configs applicable to your test suite. ``` ## Generating a custom Workload Pattern There are 2 ways to generate a workload pattern 1.Programmatically You can create a DAG of operations programmatically - take a look at `WorkflowDagGenerator` class. Once you're ready with the DAG you want to execute, simply pass the class name as follows: ``` spark-submit ... ... --class org.apache.hudi.integ.testsuite.HoodieTestSuiteJob --workload-generator-classname org.apache.hudi.integ.testsuite.dag.scheduler.<your_workflowdaggenerator> ... ``` 2.YAML file Choose to write up the entire DAG of operations in YAML, take a look at `simple-deltastreamer.yaml` or `simple-deltastreamer.yaml`. Once you're ready with the DAG you want to execute, simply pass the yaml file path as follows: ``` spark-submit ... ... --class org.apache.hudi.integ.testsuite.HoodieTestSuiteJob --workload-yaml-path /path/to/your-workflow-dag.yaml ... ``` ## Building the test suite The test suite can be found in the `hudi-integ-test` module. Use the `prepare_integration_suite.sh` script to build the test suite, you can provide different parameters to the script. ``` shell$ ./prepare_integration_suite.sh --help Usage: prepare_integration_suite.sh --spark-command, prints the spark command -h, hdfs-version -s, spark version -p, parquet version -a, avro version -s, hive version ``` ``` shell$ ./prepare_integration_suite.sh .... .... Final command : mvn clean install -DskipTests ``` ## Running on the cluster or in your local machine Copy over the necessary files and jars that are required to your cluster and then run the following spark-submit command after replacing the correct values for the parameters. NOTE : The properties-file should have all the necessary information required to ingest into a Hudi dataset. For more information on what properties need to be set, take a look at the test suite section under demo steps. ``` shell$ ./prepare_integration_suite.sh --spark-command spark-submit --master prepare_integration_suite.sh --deploy-mode --properties-file --class org.apache.hudi.integ.testsuite.HoodieTestSuiteJob target/hudi-integ-test-0.6 .0-SNAPSHOT.jar --source-class --source-ordering-field --input-base-path --target-base-path --target-table --props --storage-type --payload-class --workload-yaml-path --input-file-size --<deltastreamer-ingest> ``` ## Running through a test-case (local) Take a look at the `TestHoodieTestSuiteJob` to check how you can run the entire suite using JUnit. ## Running an end to end test suite in Local Docker environment Start the Hudi Docker demo: ``` docker/setup_demo.sh ``` NOTE: We need to make a couple of environment changes for Hive 2.x support. This will be fixed once Hudi moves to Spark 3.x. Execute below if you are using Hudi query node in your dag. If not, below section is not required. Also, for longer running tests, go to next section. ``` docker exec -it adhoc-2 bash cd /opt/spark/jars rm /opt/spark/jars/hive* rm spark-hive-thriftserver_2.11-2.4.4.jar wget https://repo1.maven.org/maven2/org/apache/spark/spark-hive-thriftserver_2.12/3.0.0-preview2/spark-hive-thriftserver_2.12-3.0.0-preview2.jar wget https://repo1.maven.org/maven2/org/apache/hive/hive-common/2.3.1/hive-common-2.3.1.jar wget https://repo1.maven.org/maven2/org/apache/hive/hive-exec/2.3.1/hive-exec-2.3.1-core.jar wget https://repo1.maven.org/maven2/org/apache/hive/hive-jdbc/2.3.1/hive-jdbc-2.3.1.jar wget https://repo1.maven.org/maven2/org/apache/hive/hive-llap-common/2.3.1/hive-llap-common-2.3.1.jar wget https://repo1.maven.org/maven2/org/apache/hive/hive-metastore/2.3.1/hive-metastore-2.3.1.jar wget https://repo1.maven.org/maven2/org/apache/hive/hive-serde/2.3.1/hive-serde-2.3.1.jar wget https://repo1.maven.org/maven2/org/apache/hive/hive-service/2.3.1/hive-service-2.3.1.jar wget https://repo1.maven.org/maven2/org/apache/hive/hive-service-rpc/2.3.1/hive-service-rpc-2.3.1.jar wget https://repo1.maven.org/maven2/org/apache/hive/shims/hive-shims-0.23/2.3.1/hive-shims-0.23-2.3.1.jar wget https://repo1.maven.org/maven2/org/apache/hive/shims/hive-shims-common/2.3.1/hive-shims-common-2.3.1.jar wget https://repo1.maven.org/maven2/org/apache/hive/hive-storage-api/2.3.1/hive-storage-api-2.3.1.jar wget https://repo1.maven.org/maven2/org/apache/hive/hive-shims/2.3.1/hive-shims-2.3.1.jar wget https://repo1.maven.org/maven2/org/json/json/20090211/json-20090211.jar cp /opt/hive/lib/log* /opt/spark/jars/ rm log4j-slf4j-impl-2.6.2.jar cd /opt ``` Copy the integration tests jar into the docker container ``` docker cp packaging/hudi-integ-test-bundle/target/hudi-integ-test-bundle-0.11.0-SNAPSHOT.jar adhoc-2:/opt ``` ``` docker exec -it adhoc-2 /bin/bash ``` Clean the working directories before starting a new test: ``` hdfs dfs -rm -r /user/hive/warehouse/hudi-integ-test-suite/output/ hdfs dfs -rm -r /user/hive/warehouse/hudi-integ-test-suite/input/ ``` Launch a Copy-on-Write job: ``` # COPY_ON_WRITE tables ========================= ## Run the following command to start the test suite spark-submit \ --conf spark.task.cpus=1 \ --conf spark.executor.cores=1 \ --conf spark.task.maxFailures=100 \ --conf spark.memory.fraction=0.4 \ --conf spark.rdd.compress=true \ --conf spark.kryoserialize