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Help Center/ MapReduce Service/ User Guide (Kuala Lumpur Region)/ MRS Cluster Component Operation Guide/ Using CarbonData (for MRS 3.x or Later)/ CarbonData Performance Tuning/ Tuning Guidelines

Tuning Guidelines

Updated on 2022-08-12 GMT+08:00

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Query Performance Tuning

There are various parameters that can be tuned to improve the query performance in CarbonData. Most of the parameters focus on increasing the parallelism in processing and optimizing system resource usage.

Spark executor count: Executors are basic entities of parallelism in Spark. Raising the number of executors can increase the amount of parallelism in the cluster. For details about how to configure the number of executors, see the Spark documentation.
Executor core: The number of concurrent tasks that an executor can run are controlled in each executor. Increasing the number of executor cores will add more concurrent processing tasks to improve performance.
HDFS block size: CarbonData assigns query tasks by allocating different blocks to different executors for processing. HDFS block is the partition unit. CarbonData maintains a global block level index in Spark driver, which helps to reduce the quantity of blocks that need to be scanned for a query. Higher block size means higher I/O efficiency and lower global index efficiency. Reversely, lower block size means lower I/O efficiency, higher global index efficiency, and greater memory consumption.
Number of scanner threads: Scanner threads control the number of parallel data blocks that are processed by each task. By increasing the number of scanner threads, you can increase the number of data blocks that are processed in parallel to improve performance. The carbon.number.of.cores parameter in the carbon.properties file is used to configure the number of scanner threads. For example, carbon.number.of.cores = 4.
B-Tree caching: The cache memory can be optimized using the B-Tree least recently used (LRU) caching. In the driver, the B-Tree LRU caching configuration helps free up the cache by releasing table segments which are not accessed or not used. Similarly, in the executor, the B-Tree LRU caching configuration will help release table blocks that are not accessed or used. For details, see the description of carbon.max.driver.lru.cache.size and carbon.max.executor.lru.cache.size in Table 2.

CarbonData Query Process

When CarbonData receives a table query task, for example query for table A, the index data of table A will be loaded to the memory for the query process. When CarbonData receives a query task for table A again, the system does not need to load the index data of table A.

When a query is performed in CarbonData, the query task is divided into several scan tasks, namely, task splitting based on HDFS blocks. Scan tasks are executed by executors on the cluster. Tasks can run in parallel, partially parallel, or in sequence, depending on the number of executors and configured number of executor cores.

Some parts of a query task can be processed at the individual task level, such as select and filter. Some parts of a query task can be processed at the individual task level, such as group-by, count, and distinct count.

Some operations cannot be performed at the task level, such as Having Clause (filter after grouping) and sort. Operations which cannot be performed at the task level or can be only performed partially at the task level require data (partial results) transmission across executors on the cluster. The transmission operation is called shuffle.

The more the tasks are, the more data needs to be shuffled. This affects query performance.

The number of tasks is depending on the number of HDFS blocks and the number of blocks is depending on the size of each block. You are advised to configure proper HDFS block size to achieve a balance among increased parallelism, the amount of data to be shuffled, and the size of aggregate tables.

Relationship Between Splits and Executors

If the number of splits is less than or equal to the executor count multiplied by the executor core count, the tasks are run in parallel. Otherwise, some tasks can start only after other tasks are complete. Therefore, ensure that the executor count multiplied by executor cores is greater than or equal to the number of splits. In addition, make sure that there are sufficient splits so that a query task can be divided into sufficient subtasks to ensure concurrency.

Configuring Scanner Threads

The scanner threads property decides the number of data blocks to be processed. If there are too many data blocks, a large number of small data blocks will be generated, affecting performance. If there are few data blocks, the parallelism is poor and the performance is affected. Therefore, when determining the number of scanner threads, you are advised to consider the average data size within a partition and select a value that makes the data block not small. Based on experience, you are advised to divide a single block size (unit: MB) by 250 and use the result as the number of scanner threads.

The number of actual available vCPUs is an important factor to consider when you want to increase the parallelism. The number of vCPUs that conduct parallel computation must not exceed 75% to 80% of actual vCPUs.

The number of vCPUs is approximately equal to:

Number of parallel tasks x Number of scanner threads. Number of parallel tasks is the smaller value of number of splits or executor count x executor cores.

Data Loading Performance Tuning

Tuning of data loading performance is different from that of query performance. Similar to query performance, data loading performance depends on the amount of parallelism that can be achieved. In case of data loading, the number of worker threads decides the unit of parallelism. Therefore, more executors mean more executor cores and better data loading performance.

To achieve better performance, you can configure the following parameters in HDFS.

**Table 1** HDFS configuration
Parameter	Recommended Value
dfs.datanode.drop.cache.behind.reads	false
dfs.datanode.drop.cache.behind.writes	false
dfs.datanode.sync.behind.writes	true

Compression Tuning

CarbonData uses a few lightweight compression and heavyweight compression algorithms to compress data. Although these algorithms can process any type of data, the compression performance is better if the data is ordered with similar values being together.

During data loading, data is sorted based on the order of columns in the table to achieve good compression performance.

Since CarbonData sorts data in the order of columns defined in the table, the order of columns plays an important role in the effectiveness of compression. If the low cardinality dimension is on the left, the range of data partitions after sorting is small and the compression efficiency is high. If a high cardinality dimension is on the left, a range of data partitions obtained after sorting is relatively large, and compression efficiency is relatively low.

Memory Tuning

CarbonData provides a mechanism for memory tuning where data loading depends on the columns needed in the query. Whenever a query command is received, columns required by the query are fetched and data is loaded for those columns in memory. During this operation, if the memory threshold is reached, the least used loaded files are deleted to release memory space for columns required by the query.

Parent topic: CarbonData Performance Tuning

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Next topic: Suggestions for Creating CarbonData Tables

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