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Community Blog Jingdong: Flink SQL Optimization Practice

Jingdong: Flink SQL Optimization Practice

This article focuses on the optimization measures of Jingdong in Flink SQL tasks, focusing on the aspects of shuffle, join mode selection, object reuse, and UDF reuse.

The authors of this article are Zhang Ying and Duan Xuehao of the Jingdong Algorithm Service Department. Li Rui, an Alibaba Technical Expert in Apache Hive PMC, helped proofread it. The main contents are listed below:

  1. Background
  2. Optimization of Flink SQL
  3. Summary

Visit the GitHub page. You are welcome to give it a like and stars!

1. Background

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The current data processing process recommended by Jingdong Search is shown in the figure above. We can see that real-time and offline data are separated. Most of the offline data processing uses Hive/Spark, while most of the real-time data processing uses Flink/Storm.

As a result, users need to maintain two sets of environments and two sets of codes in a business engine. Many common characters cannot be reused, and it is difficult to guarantee the quality and consistency of data. Moreover, since the underlying data models of the stream batch are inconsistent, it needs a large amount of patchwork logic. For data consistency, a large amount of data comparison, such as year-on-year, link relative ratio, and secondary processing needs to be carried out, which is extremely inefficient and prone to errors.

Flink SQL, which supports batch and stream integration, can solve this pain point. Therefore, we decided to introduce Flink to solve this problem.

In most jobs, especially Flink jobs, the optimization of execution efficiency has been the key to Flink task optimization, which is especially important in the case of daily data increment of Jingdong reaching petabytes.

Developers that have written some SQL work know that for Flink SQL, the same UDF will be called repeatedly in some cases, which is very unfriendly to some resource-consuming tasks. In addition, the impact on execution efficiency can be measured by shuffle, join, and failover strategy perspectives. The Flink task debugging process is also very complicated, especially for some enterprises with isolated online machines.

For this purpose, we have implemented embedded Derby as a metadata storage database of Hive (allowEmbedded). In terms of task recovery, batch jobs do not have a checkpoint mechanism to implement failover, but the unique region strategy of Flink can enable batch jobs to recover quickly. This article also introduces relevant optimization measures such as object reuse.

2. Optimization of Flink SQL

1. UDF Reuse

The following situation occurs in Flink SQL tasks: If the same UDF appears in both LogicalProject and Where conditions, the UDF will be called multiple times (please see this link). However, if the UDF consumes a lot of CPU or memory, this redundant calculation will affect the performance significantly. Therefore, we hope to cache the UDF results for direct use next time. It is important to consider when designing. (Very Important Note: Please ensure that LogicalProject and subtask chain of Where conditions are joined together.)

  • There may be multiple subtasks in a taskmanager, so this cache is either thread (THREAD LOCAL) level or tm level.
  • The cache must be cleared in the close method to prevent some situations from causing the logic of clearing the cache to fail.
  • The selected cache is best to actively control the size and prevent the memory from increasing indefinitely. As for the timeout period, it is suggested to configure it, but it is better not to be less than the time that UDF calls successively.
  • As mentioned above, there may be multiple subtasks in a tm, which is equivalent to a multi-threaded environment in tm. First of all, the thread of the cache needs to be safe. Then, we can judge whether we need to lock according to business status.

According to the considerations above, we use guava cache to cache the UDF results and then go to the cache directly to get the data when calling, which may reduce the task consumption as much as possible. The following is a simple use case (the maximum use size and timeout are set at the same time, but there is no write lock):

public class RandomFunction extends ScalarFunction {
    private static Cache<String, Integer> cache = CacheBuilder.newBuilder()
            .maximumSize(2)
            .expireAfterWrite(3, TimeUnit.SECONDS)
            .build();

    public int eval(String pvid) {
        profileLog.error("RandomFunction invoked:" + atomicInteger.incrementAndGet());
        Integer result = cache.getIfPresent(pvid);
        if (null == result) {
            int tmp = (int)(Math.random() * 1000);
            cache.put("pvid", tmp);
            return tmp;
        }
        return result;
    }
    @Override
    public void close() throws Exception {
        super.close();
        cache.cleanUp();
    }
}

2. Unit Testing

You may wonder why unit testing is also included in optimization. We know the Flink task debugging process is very complicated, especially for some enterprises with isolated online machines. Jingdong's local environment cannot access the task server, so we spent a lot of time uploading jar packages, checking logs, and doing other behaviors in the initial stage of debugging tasks.

The embedded Derby is implemented as a metadata storage database of Hive (allowEmbedded) to reduce the debugging time of the task and increase the development efficiency of the code developer, which is a method to optimize the development time. The logic includes the following steps:

First, create a Hive Conf:

public static HiveConf createHiveConf() {
    ClassLoader classLoader = new HiveOperatorTest().getClass().getClassLoader();
    HiveConf.setHiveSiteLocation(classLoader.getResource(HIVE_SITE_XML));

    try {
        TEMPORARY_FOLDER.create();
        String warehouseDir = TEMPORARY_FOLDER.newFolder().getAbsolutePath() + "/metastore_db";
        String warehouseUri = String.format(HIVE_WAREHOUSE_URI_FORMAT, warehouseDir);

        HiveConf hiveConf = new HiveConf();
        hiveConf.setVar(
                HiveConf.ConfVars.METASTOREWAREHOUSE,
                TEMPORARY_FOLDER.newFolder("hive_warehouse").getAbsolutePath());
        hiveConf.setVar(HiveConf.ConfVars.METASTORECONNECTURLKEY, warehouseUri);

        hiveConf.set("datanucleus.connectionPoolingType", "None");
        hiveConf.set("hive.metastore.schema.verification", "false");
        hiveConf.set("datanucleus.schema.autoCreateTables", "true");
        return hiveConf;
    } catch (IOException e) {
        throw new CatalogException("Failed to create test HiveConf to HiveCatalog.", e);
    }
}

Next, create Hive Catalog (call the embedded interface by reflection):

public static void createCatalog() throws Exception{
    Class clazz = HiveCatalog.class;
    Constructor c1 = clazz.getDeclaredConstructor(new Class[]{String.class, String.class, HiveConf.class, String.class, boolean.class});
    c1.setAccessible(true);
    hiveCatalog = (HiveCatalog)c1.newInstance(new Object[]{"test-catalog", null, createHiveConf(), "2.3.4", true});
    hiveCatalog.open();
}

Create tableEnvironment: (It is the same as the official website.)

EnvironmentSettings settings = EnvironmentSettings.newInstance().useBlinkPlanner().inBatchMode().build();
TableEnvironment tableEnv = TableEnvironment.create(settings);
TableConfig tableConfig = tableEnv.getConfig();
Configuration configuration = new Configuration();
configuration.setInteger("table.exec.resource.default-parallelism", 1);
tableEnv.registerCatalog(hiveCatalog.getName(), hiveCatalog);
tableEnv.useCatalog(hiveCatalog.getName());

Finally, close the Hive Catalog:

public static void closeCatalog() {
    if (hiveCatalog != null) {
        hiveCatalog.close();
    }
}

In addition, building a suitable data set is also an important function for unit testing. We have implemented CollectionTableFactory to build a suitable data set using the following methods:

CollectionTableFactory.reset();
CollectionTableFactory.initData(Arrays.asList(Row.of("this is a test"), Row.of("zhangying480"), Row.of("just for test"), Row.of("a test case")));
StringBuilder sbFilesSource = new StringBuilder();
sbFilesSource.append("CREATE temporary TABLE db1.`search_realtime_table_dump_p13`(" + "  `pvid` string) with ('connector.type'='COLLECTION','is-bounded' = 'true')");
tableEnv.executeSql(sbFilesSource.toString());

3. Selection of Join Mode

Traditional offline Batch SQL (SQL for bounded data sets) has three basic implementations, Nested-loop Join, Sort-Merge Join, and Hash Join.

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  • Nested-loop Join is the most straightforward way to load two data sets into memory and use embedded traversal to compare whether the elements in the two data sets meet the Join conditions one by one. NestedLoopJoin has the lowest time and space efficiency, and it can be disabled using: table.exec.disabled-operators:NestedLoopJoin.

The following two pictures show the results before and after disabling (if the disabling does not take effect, check if it is Equi-Join first):

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  • The Sort-Merge Join is divided into two stages: Sort and Merge. First, the two data sets are sorted respectively, and then the two sets are traversed and matched separately. The process is similar to the merge and sort process. (Sort-Merge Join requires the sorting of two datasets, but it can be used as an optimization scheme if the two inputs are ordered datasets.)
  • Hash Join is also divided into two phases: First, one dataset is converted to a Hash Table, and then another dataset element is traversed and matched with the elements in the Hash Table.

    • The first stage and the first data set are called the build stage and build table, respectively.
    • The second stage and the second data set are called the probe stage and the probe table, respectively.

Hash Join is more efficient but requires a large space. It is usually used as an optimization scheme when one of the join tables is a small one suitable for putting into memory (Disk overflow is allowed.)

Note: Sort-Merge Join and Hash Join apply to Equi-Join. (They both use equals as comparison operators for Join conditions.)

Flink has made some subdivisions on top of join, including:

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  • Repartition-Repartition Strategy: The two joined datasets are partitioned with the same partition function for their keys, and data are sent through the network.
  • Broadcast-Forward Strategy: A large dataset is not processed, and another relatively small dataset is copied to a machine with some data in the cluster.

As we all know, batch shuffle is time-consuming:

  • If there is a big gap between the two data sets, the Broadcast-Forward strategy is recommended.
  • If the two data sets are similar, the Repartition-Repartition strategy is recommended.

We can use the: table.optimizer.join.broadcast-threshold to set the table size that uses broadcast. If it is set to "-1," broadcast is disabled.

The following figure shows the results before and after disabling:

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4. Multiple Input

In Flink SQL tasks, reducing shuffle can improve the throughput of SQL tasks. In real-world business scenarios, we often encounter situations that the upstream data have met the data distribution requirements. (For example, multiple join operators are in succession, where the keys are the same.) In this case, a forward shuffle of Flink is a redundant shuffle, and we hope to chain these operators together. Flink 1.12 introduces the features of multiple input, which can eliminate most of the unnecessary forward shuffles and chain the source operators together.

table.optimizer.multiple-input-enabled: true

The following figure shows the topology graph with multiple input and without it. (The operator chain function has been turned on.)

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5. Object Reuse

The upstream and downstream operators go through the serialization/deserialization/copy stages for data transmission. This behavior greatly affects the performance of Flink SQL programs. We can improve the performance by enabling object reuse. However, it is very dangerous in DataStream because modifying the object in the next operator could accidentally affect the object in the operator above.

However, the Table/SQL API of Flink is very secure and can be enabled in the following ways:

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.getConfig().enableObjectReuse();

It can also be enabled by setting: pipeline-object-reuse:true

Why does enabling object reuse bring such a big performance boost? In Blink planner, data exchange between two operators of the same task will eventually call BinaryString#copy. Looking at the implementation code, we can find that BinaryString#copy needs to copy the bytes of the underlying MemorySegment. It can improve efficiency by enabling object reuse to avoid replication.

The following figure shows the corresponding flame graph when there is no open object reuse:

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6. Failover Policies for SQL Tasks

In batch task mode, checkpoint and its related features are all unavailable. Therefore, the checkpoint-based failover policies for real-time tasks cannot be used in batch tasks. However, batch tasks allow Tasks to communicate through Blocking Shuffle. When a Task fails due to unknown reasons, Blocking Shuffle stores all the data required by this Task. Therefore, we only need to restart this Task and all downstream tasks connected to it through Pipeline Shuffle:

jobmanager.execution.failover-strategy:region (The finished operator can be restored directly.)

table.exec.shuffle-mode:ALL_EDGES_BLOCKING (shuffle policy)

7. Shuffle

The shuffle in Flink is divided into pipeline shuffle and blocking shuffle.

  • Pipeline shuffle has good performance, but it requires high resources and poor fault tolerance. (The operator will be placed into the previous region. For batch tasks, if there is a problem with this operator, it will be restored from the previous region.)
  • Blocking shuffle is the traditional batch shuffle, which will put data on a disk. This shuffle has good fault tolerance, but it will generate a large number of disks and network IO. (It is suggested to use blocking shuffle to reduce the concerns.) Blocking shuffle is divided into hash shuffle and sort shuffle.

    • If your disk is SSD format and the concurrency is not high, you can choose to use hash shuffle. It generates more files and reads more randomly, which has a great impact on disk IO.
    • If your disk is in SATA format and the concurrency is high, you can choose to use sort-merge shuffle. This shuffle generates less data, reads sequentially, and does not generate a large number of disk IO, but the overhead will be greater (sort merge).

Corresponding Control Parameters:

For table.exec.shuffle-mode, this parameter contains multiple parameters. The default is ALL_EDGES_BLOCKING, which means all edges will use blocking shuffle. However, we can try the POINTWISE_EDGES_PIPELINED, which means forward and rescale edges will start pipeline mode automatically.

For taskmanager.network.sort-shuffle.min-parallelism, we set this parameter to less than the parallelism, so we can open the sort-merge shuffle. The setting of this parameter requires you to consider some other situations, which can be set according to the official website.

3. Summary

This article focuses on the optimization measures of Jingdong in Flink SQL tasks, focusing on the aspects of shuffle, join mode selection, object reuse, and UDF reuse. I would like to thank all my colleagues (especially Fu Haitao of the Jingdong Real-Time Computing Research and Development Department) for their support and help.

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