Cloudera’s Introduction to Apache Kudu training teaches students the basics of Apache Kudu, a data storage system for the Hadoop platform that is optimized for analytical queries. By Grant Henke. This is the mode we used for testing throughput and latency of Apache Kudu block cache. Tung Vs Tung Vs. 124 10 10 bronze badges. Students will learn how to create, manage, and query Kudu tables, and to develop Spark applications that use Kudu. While the Apache Kudu project provides client bindings that allow users to mutate and fetch data, more complex access patterns are often written via SQL and compute engines. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. DataEngConf. Les données y sont stockées sous forme de fichiers bruts. performance apache-spark apache-kudu data-ingestion. The test was setup similar to the random access above with 1000 operations run in loop and runtimes measured which can be seen in Table 2 below: Just laying down my thoughts about Apache Kudu based on my exploration and experiments. import org.apache.kudu.spark.kudu.KuduContext; import org.apache.kudu.client.CreateTableOptions; CreateTableOptions kuduTableOptions = new CreateTableOptions(); // create a scala Seq of table's primary keys, //create a table with same schema as data frame, CREATE EXTERNAL TABLE IF NOT EXISTS , https://www.cloudera.com/documentation/kudu/5-10-x/topics/kudu_impala.html, https://github.com/hortonworks/hive-testbench, Productionalizing Spark Streaming Applications, Machine Learning with Microsoft’s Azure ML — Credit Classification, Improving your Apache Spark Application Performance, Optimizing Conversion between Spark and Pandas DataFrames using Apache PyArrow, Installing Apache Kafka on Cloudera’s Quickstart VM, AWS Cloud Solution: DynamoDB Tables Backup in S3 (Parquet). One of such platforms is Apache Kudu that can utilize DCPMM for its internal block cache. It isn't an this or that based on performance, at least in my opinion. Kudu’s architecture is shaped towards the ability to provide very good analytical performance, while at the same time being able to receive a continuous stream of inserts and updates. If we have a data frame which we wish to store to Kudu, we can do so as follows: Unsupported Datatypes: Some complex datatypes are unsupported by Kudu and creating tables using them would through exceptions when loading via Spark. These characteristics of Optane DCPMM provide a significant performance boost to big data storage platforms that can utilize it for caching. Where possible, Impala pushes down predicate evaluation to Kudu, so that predicates are evaluated as close as possible to the data. Stack Overflow Public questions & answers; Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Jobs Programming & related technical career opportunities; Talent Recruit tech talent & build your employer brand; Advertising Reach developers & technologists worldwide; About the company Kudu builds upon decades of database research. Anyway, my point is that Kudu is great for somethings and HDFS is great for others. asked Aug 13 '18 at 4:55. Doing so could negatively impact performance, memory and storage. In the below example script if table movies already exist then Kudu backed table can be created as follows: Unsupported data-types: When creating a table from an existing hive table if the table has VARCHAR(), DECIMAL(), DATE and complex data types(MAP, ARRAY, STRUCT, UNION) then these are not supported in kudu. Links are not permitted in comments. When Kudu starts, it checks each configured data directory, expecting either for all to be initialized or for all to be empty. These tasks include flushing data from memory to disk, compacting data to improve performance, freeing up disk space, and more. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Maintenance manager The maintenance manager schedules and runs background tasks. DCPMM provides two operating modes: Memory and App Direct. Overall I can conclude that if the requirement is for a storage which performs as well as HDFS for analytical queries with the additional flexibility of faster random access and RDBMS features such as Updates/Deletes/Inserts, then Kudu could be considered as a potential shortlist. Technical. We need to create External Table if we want to access via Impala: The table created in Kudu using the above example resides in Kudu storage only and is not reflected as an Impala table. From the tests, I can see that although it does take longer to initially load data into Kudu as compared to HDFS, it does give a near equal performance when it comes to running analytical queries and better performance for random access to data. Adding DCPMM modules for Kudu block cache could significantly speed up queries that repeatedly request data from the same time window. Reduce DRAM footprint required for Apache Kudu, Keep performance as close to DRAM speed as possible, Take advantage of larger cache capacity to cache more data and improve the entire system’s performance, The Persistent Memory Development Kit (PMDK), formerly known as NVML, is a growing collection of libraries and tools. San Francisco, CA, USA. Kudu is not an OLTP system, but it provides competitive random-access performance if you have some subset of data that is suitable for storage in memory. Currently the Kudu block cache does not support multiple nvm cache paths in one tablet server. Apache Kudu Ecosystem. This is a non-exhaustive list of projects that integrate with Kudu to enhance ingest, querying capabilities, and orchestration. Apache Kudu is an open-source columnar storage engine. Apache Kudu Ecosystem. As far as accessibility is concerned I feel there are quite some options. Testing Apache Kudu Applications on the JVM. The idea behind this article was to document my experience in exploring Apache Kudu, understanding its limitations if any and also running some experiments to compare the performance of Apache Kudu storage against HDFS storage. Table 1. shows time in secs between loading to Kudu vs Hdfs using Apache Spark. Since support for persistent memory has been integrated into memkind, we used it in the Kudu block cache persistent memory implementation. It is compatible with most of the data processing frameworks in the Hadoop environment. Including all optimizations, relative to Apache Kudu 1.11.1, the geometric mean performance increase was approximately 2.5x. By default, Kudu stores its minidumps in a subdirectory of its configured glog directory called minidumps. scan-to-seek, see section 4.1 in [1]). Apache Kudu is a new, open source storage engine for the Hadoop ecosystem that enables extremely high-speed analytics without imposing data-visibility latencies. The recommended target size for tablets is under 10 GiB. combines support for multiple types of volatile memory into a single, convenient API. Introducing Apache Kudu (incubating) Kudu is a columnar storage manager developed for the Hadoop platform. | Terms & Conditions Below is the YCSB workload properties for these two datasets. These tasks include flushing data from memory to disk, compacting data to improve performance, freeing up disk space, and more. Memory mode is volatile and is all about providing a large main memory at a cost lower than DRAM without any changes to the application, which usually results in cost savings. So we need to bind two DCPMM sockets together to maximize the block cache capacity. Comparing Kudu with HDFS Comma Separated storage file: Observations: Chart 2 compared the kudu runtimes (same as chart 1) against HDFS Comma separated storage. The idea behind this article was to document my experience in exploring Apache Kudu, understanding its limitations if any and also running some experiments to compare the performance of Apache Kudu storage against HDFS storage. Recently, I wanted to stress-test and benchmark some changes to the Kudu RPC server, and decided to use YCSB as a way to generate reasonable load. DCPMM modules offer larger capacity for lower cost than DRAM. Including all optimizations, relative to Apache Kudu 1.11.1, the geometric mean performance increase was approximately 2.5x. The TPC-H Suite includes some benchmark analytical queries. Druid and Apache Kudu are both open source tools. Note that this only creates the table within Kudu and if you want to query this via Impala you would have to create an external table referencing this Kudu table by name. Some benefits from persistent memory block cache: Intel Optane DC persistent memory (Optane DCPMM) breaks the traditional memory/storage hierarchy and scales up the compute server with higher capacity persistent memory. HDFS, Hadoop Distributed File System, est souvent considéré comme la brique indispensable d’un datalake et joue le rôle de la couche de persistance la plus basse. A new open source Apache Hadoop ecosystem project, Apache Kudu completes Hadoop's storage layer to enable fast analytics on fast data ... Benchmarking and Improving Kudu Insert Performance with YCSB.