Let’s begin with discussing the current query flow in Kudu. Technical. Cloud Serving Benchmark (YCSB) is an open-source test framework that is often used to compare relative performance of NoSQL databases. My Personal Experience on Apache Kudu performance. These tasks include flushing data from memory to disk, compacting data to improve performance, freeing up disk space, and more. … Since support for persistent memory has been integrated into memkind, we used it in the Kudu block cache persistent memory implementation. Notice Revision #20110804. Performance considerations: Kudu stores each value in as few bytes as possible depending on the precision specified for the decimal column. To test this assumption, we used YCSB benchmark to compare how Apache Kudu performs with block cache in DRAM to how it performs when using Optane DCPMM for block cache. Observations: From the table above we can see that Small Kudu Tables get loaded almost as fast as Hdfs tables. The runtimes for these were measured for Kudu 4, 16 and 32 bucket partitioned data as well as for HDFS Parquet stored Data. For small (100GB) test (dataset smaller than DRAM capacity), we have observed similar performance in DCPMM and DRAM-based configurations. C’est la partie immuable de notre dataset. When in doubt about introducing a new dependency on any boost functionality, it is best to email dev@kudu.apache.org to start a discussion. Apache Kudu was first announced as a public beta release at Strata NYC 2015 and reached 1.0 last fall. One of such platforms is Apache Kudu that can utilize DCPMM for its internal block cache. If the data is not found in the block cache, it will read from the disk and insert into block cache. For a complete list of trademarks, click here. Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Begun as an internal project at Cloudera, Kudu is an open source solution compatible with many data processing frameworks in the Hadoop environment. | Privacy Policy and Data Policy. Memkind combines support for multiple types of volatile memory into a single, convenient API. then Kudu would not be a good option for that. Each node has 2 x 22-Core Intel Xeon E5-2699 v4 CPUs (88 hyper-threaded cores), 256GB of DDR4-2400 RAM and 12 x 8TB 7,200 SAS HDDs. DataEngConf. 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. More detail is available at. The kudu storage engine supports access via Cloudera Impala, Spark as well as Java, C++, and Python APIs. Simplified flow version is; kafka -> flink -> kudu -> backend -> customer. 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. It isn't an this or that based on performance, at least in my opinion. Including all optimizations, relative to Apache Kudu 1.11.1, the geometric mean performance increase was approximately 2.5x. It promises low latency random access and efficient execution of analytical queries. Kudu Tablet Servers store and deliver data to clients. 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. DCPMM modules offer larger capacity for lower cost than DRAM. San Jose, CA, USA. By Greg Solovyev. San Francisco, CA, USA. One machine had DRAM and no DCPMM. Apache Impala Apache Kudu Apache Sentry Apache Spark. On dit que la donnée y est rangée en … It may automatically evict entries to make room for new entries. For more complete information visit www.intel.com/benchmarks. asked Aug 13 '18 at 4:55. Kudu Tablet Servers store and deliver data to clients. These tasks include flushing data from memory to disk, compacting data to improve performance, freeing up disk space, and more. To evaluate the performance of Apache Kudu, we ran YCSB read workloads on two machines. Tung Vs Tung Vs. 124 10 10 bronze badges. The Kudu team allows line lengths of 100 characters per line, rather than Google’s standard of 80. Already present: FS layout already exists. This can cause performance issues compared to the log block manager even with a small amount of data and it’s impossible to switch between block managers without wiping and reinitializing the tablet servers. 5,143 6 6 gold badges 21 21 silver badges 32 32 bronze badges. The Yahoo! 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. The Persistent Memory Development Kit (PMDK), formerly known as NVML, is a growing collection of libraries and tools. The small dataset is designed to fit entirely inside Kudu block cache on both machines. To query the table via Impala we must create an external table pointing to the Kudu table. Intel’s compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. 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. The Kudu tables are hash partitioned using the primary key. More detail is available at https://pmem.io/pmdk/. If a Kudu table is created using SELECT *, then the incompatible non-primary key columns will be dropped in the final table. High-efficiency queries. Any attempt to select these columns and create a kudu table will result in an error. So, we saw the apache kudu that supports real-time upsert, delete. The large dataset is designed to exceed the capacity of Kudu block cache on DRAM, while fitting entirely inside the block cache on DCPMM. 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. So we need to bind two DCPMM sockets together to maximize the block cache capacity. My Personal Experience on Apache Kudu performance. AWS S3), Apache Kudu and HBase. Analytic use-cases almost exclusively use a subset of the columns in the queriedtable and generally aggregate values over a broad range of rows. Maintenance manager The maintenance manager schedules and runs background tasks. The authentication features introduced in Kudu 1.3 place the following limitations on wire compatibility between Kudu 1.13 and versions earlier than 1.3: This allows Apache Kudu to reduce the overhead by reading data from low bandwidth disk, by keeping more data in block cache. Druid and Apache Kudu are both open source tools. 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. Good documentation can be found here https://www.cloudera.com/documentation/kudu/5-10-x/topics/kudu_impala.html. Doing so could negatively impact performance, memory and storage. Primary Key: Primary keys must be specified first in the table schema. It has higher bandwidth & lower latency than storage like SSD or HDD and performs comparably with DRAM. Anyway, my point is that Kudu is great for somethings and HDFS is great for others. that can utilize DCPMM for its internal block cache. Apache Hadoop and associated open source project names are trademarks of the Apache Software Foundation. US: +1 888 789 1488 Let's start with adding the dependencies, Next, create a KuduContext as shown below. YCSB workload shows that DCPMM will yield about a 1.66x performance improvement in throughput and 1.9x improvement in read latency (measured at 95%) over DRAM. The idea behind this experiment was to compare Apache Kudu and HDFS in terms of loading data and running complex Analytical queries. This summer I got the opportunity to intern with the Apache Kudu team at Cloudera. These tasks include flushing data from memory to disk, compacting data to improve performance, freeing up disk space, and more. This post was authored by guest author Cheng Xu, Senior Architect (Intel), as well as Adar Lieber-Dembo, Principal Engineer (Cloudera) and Greg Solovyev, Engineering Manager (Cloudera). Contact Us Comparing Kudu with HDFS Comma Separated storage file: Observations: Chart 2 compared the kudu runtimes (same as chart 1) against HDFS Comma separated storage. Apache Kudu (incubating): New Apache Hadoop Storage for Fast Analytics on Fast Data. You can find more information about Time Series Analytics with Kudu on Cloudera Data Platform at https://www.cloudera.com/campaign/time-series.html. The runtime for each query was recorded and the charts below show a comparison of these run times in sec. 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. A columnar storage manager developed for the Hadoop platform. Kudu boasts of having much lower latency when randomly accessing a single row. 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. combines support for multiple types of volatile memory into a single, convenient API. CDH 6.3 Release: What’s new in Kudu. For the persistent memory block cache, we allocated space for the data from persistent memory instead of DRAM. we have ad-hoc queries a lot, we have to aggregate data in query time. Also, Primary key columns cannot be null. For the persistent memory block cache, we allocated space for the data from persistent memory instead of DRAM. Try to keep under 80 where possible, but you can spill over to 100 or so if necessary. These characteristics of Optane DCPMM provide a significant performance boost to big data storage platforms that can utilize it for caching. Staying within these limits will provide the most predictable and straightforward Kudu experience. Performing insert, updates and deletes on the data: It is also possible to create a kudu table from existing Hive tables using CREATE TABLE DDL. Strata Hadoop World. Each Tablet Server has a dedicated LRU block cache, which maps keys to values. You can find more information about Time Series Analytics with Kudu on Cloudera Data Platform at, https://www.cloudera.com/campaign/time-series.html, An A-Z Data Adventure on Cloudera’s Data Platform, The role of data in COVID-19 vaccination record keeping, How does Apache Spark 3.0 increase the performance of your SQL workloads. If the data is not found in the block cache, it will read from the disk and insert into block cache. Kudu. The other machine had both DRAM and DCPMM. Observations: Chart 1 compares the runtimes for running benchmark queries on Kudu and HDFS Parquet stored tables. Query performance is comparable to Parquet in many workloads. Kudu block cache uses internal synchronization and may be safely accessed concurrently from multiple threads. Apache Kudu background maintenance tasks. Il est compatible avec la plupart des frameworks de traitements de données de l'environnement Hadoop. To achieve the highest possible performance on modern hardware, the Kudu client used by Impala parallelizes scans across multiple tablets. Maximizing performance of Apache Kudu block cache with Intel Optane DCPMM. SparkKudu can be used in Scala or Java to load data to Kudu or read data as Data Frame from Kudu. This practical guide shows you how. 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. The core philosophy is to make the lives of developers easier by providing transactions with simple, strong semantics, without sacrificing performance or the ability to tune to different requirements. Adding DCPMM modules for Kudu … Additionally, Kudu client APIs are available in Java, Python, and C++ (not covered as part of this blog). performance apache-spark apache-kudu data-ingestion. Spark does manage to convert the VARCHAR() to a spring type, however, the other types (ARRAY, DATE, MAP, UNION, and DECIMAL) would not work. Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available security updates. By Krishna Maheshwari. This is the mode we used for testing throughput and latency of Apache Kudu block cache. So we need to bind two DCPMM sockets together to maximize the block cache capacity. The kudu storage engine supports access via Cloudera Impala, Spark as well as Java, C++, and Python APIs. open sourced and fully supported by Cloudera with an enterprise subscription for 1000 Random accesses proving that Kudu indeed is the winner when it comes to random access selections. Operational use-cases are morelikely to access most or all of the columns in a row, and … 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 These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Tuned and validated on both Linux and Windows, the libraries build on the DAX feature of those operating systems (short for Direct Access) which allows applications to access persistent memory as memory-mapped files. We can see that the Kudu stored tables perform almost as well as the HDFS Parquet stored tables, with the exception of some queries(Q4, Q13, Q18) where they take a much longer time as compared to the latter. Cheng Xu, Senior Architect (Intel), as well as Adar Lieber-Dembo, Principal Engineer (Cloudera) and Greg Solovyev, Engineering Manager (Cloudera), Intel Optane DC persistent memory (Optane DCPMM) has higher bandwidth and lower latency than SSD and HDD storage drives. Currently the Kudu block cache does not support multiple nvm cache paths in one tablet server. Refer to, https://pmem.io/2018/05/15/using_persistent_memory_devices_with_the_linux_device_mapper.html, DCPMM modules offer larger capacity for lower cost than DRAM. Kudu relies on running background tasks for many important maintenance activities. The course covers common Kudu use cases and Kudu architecture. 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. 5,143 6 6 gold badges 21 21 silver badges 32 32 bronze badges. This is a non-exhaustive list of projects that integrate with Kudu to enhance ingest, querying capabilities, and orchestration. At any given point in time, the maintenance manager … Tung Vs Tung Vs. 124 10 10 bronze badges. Thu, Mar 31, 2016. | Terms & Conditions 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. In addition to its impressive scan speed, Kudu supports many operations available in traditional databases, including real-time insert, update, and delete operations. Where possible, Impala pushes down predicate evaluation to Kudu, so that predicates are evaluated as close as possible to the data. Presented by Adar Dembo. When measuring latency of reads at the 95th percentile (reads with observed latency higher than 95% all other latencies) we have observed 1.9x gain in DCPMM-based configuration compared to DRAM-based configuration. For that reason it is not advised to just use the highest precision possible for convenience. In order to get maximum performance for Kudu block cache implementation we used the Persistent Memory Development Kit (PMDK). 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. Adding DCPMM modules for Kudu block cache could significantly speed up queries that repeatedly request data from the same time window. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Outside the US: +1 650 362 0488, © 2021 Cloudera, Inc. All rights reserved. By default, Kudu stores its minidumps in a subdirectory of its configured glog directory called minidumps. Kudu is a powerful tool for analytical workloads over time series data. CREATE TABLE new_kudu_table(id BIGINT, name STRING, PRIMARY KEY(id)), --Upsert when insert is meant to override existing row. Apache Kudu background maintenance tasks. Apache Kudu Ecosystem. One of such platforms is. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. performance apache-spark apache-kudu data-ingestion. Apache Kudu est un datastore libre et open source orienté colonne pour l'écosysteme Hadoop libre et open source. The kudu storage engine supports access via Cloudera Impala, Spark as well as Java, C++, and Python APIs.