Kudu block cache uses internal synchronization and may be safely accessed concurrently from multiple threads. 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. On dit que la donnée y est rangée en … The course covers common Kudu use cases and Kudu architecture. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. See backup for configuration details. Apache Kudu is an open-source columnar storage engine. 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. Apache Kudu 1.3.0-cdh5.11.1 was the most recent version provided with CM parcel and Kudu 1.5 was out at that time, we decided to use Kudu 1.3, which was included with the official CDH version. Kudu relies on running background tasks for many important maintenance activities. Apache Impala Apache Kudu Apache Sentry Apache Spark. Adding DCPMM modules for Kudu … Apache Kudu is an open source columnar storage engine, which enables fast analytics on fast data. Kudu builds upon decades of database research. Since Kudu supports these additional operations, this section compares the runtimes for these. Apache Kudu background maintenance tasks. Since support for persistent memory has been integrated into memkind, we used it in the Kudu block cache persistent memory implementation. By Krishna Maheshwari. This is a non-exhaustive list of projects that integrate with Kudu to enhance ingest, querying capabilities, and orchestration. There are some limitations with regards to datatypes supported by Kudu and if a use case requires the use of complex types for columns such as Array, Map, etc. Staying within these limits will provide the most predictable and straightforward Kudu experience. San Jose, CA, USA. Fine-Grained Authorization with Apache Kudu and Impala. Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available security updates. Each Tablet Server has a dedicated LRU block cache, which maps keys to values. 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. San Francisco, CA, USA. 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. In order to test this, I used the customer table of the same TPC-H benchmark and ran 1000 Random accesses by Id in a loop. CREATE TABLE new_kudu_table(id BIGINT, name STRING, PRIMARY KEY(id)), --Upsert when insert is meant to override existing row. This location can be customized by setting the --minidump_path flag. These characteristics of Optane DCPMM provide a significant performance boost to big data storage platforms that can utilize it for caching. Since support for persistent memory has been integrated into memkind, we used it in the Kudu block cache persistent memory implementation. 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. One of such platforms is Apache Kudu that can utilize DCPMM for its internal block cache. If a Kudu table is created using SELECT *, then the incompatible non-primary key columns will be dropped in the final table. Adding kudu_spark to your spark project allows you to create a kuduContext which can be used to create Kudu tables and load data to them. Strata Hadoop World. 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. Fast data made easy with Apache Kafka and Apache Kudu … Observations: Chart 1 compares the runtimes for running benchmark queries on Kudu and HDFS Parquet stored tables. Memkind combines support for multiple types of volatile memory into a single, convenient API. Kudu is a powerful tool for analytical workloads over time series data. DCPMM provides two operating modes: Memory and App Direct. Kudu relies on running background tasks for many important maintenance activities. scan-to-seek, see section 4.1 in [1]). Also, I don't view Kudu as the inherently faster option. Apache Kudu Background Maintenance Tasks Kudu relies on running background tasks for many important automatic maintenance activities. Introducing Apache Kudu (incubating) Kudu is a columnar storage manager developed for the Hadoop platform. open sourced and fully supported by Cloudera with an enterprise subscription In this talk, we present Impala's architecture in detail and discuss the integration with different storage engines and the cloud. The large dataset is designed to exceed the capacity of Kudu block cache on DRAM, while fitting entirely inside the block cache on DCPMM. Frequently used The queries were run using Impala against HDFS Parquet stored table, Hdfs comma separated storage and Kudu (16 and 32 Buckets Hash Partitions on Primary Key). The Yahoo! Apache Parquet - A free and open-source column-oriented data storage format . Your email address will not be published. More detail is available at https://pmem.io/pmdk/. combines support for multiple types of volatile memory into a single, convenient API. performance apache-spark apache-kudu data-ingestion. This allows Apache Kudu to reduce the overhead by reading data from low bandwidth disk, by keeping more data in block cache. Resolving Transactional Access/Analytic Performance Trade-offs in Apache Hadoop with Apache Kudu. For the persistent memory block cache, we allocated space for the data from persistent memory instead of DRAM. 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 Kudu tables are hash partitioned using the primary key. Any attempt to select these columns and create a kudu table will result in an error. As the library for SparkKudu is written in Scala, we would have to apply appropriate conversions such as converting JavaSparkContext to a Scala compatible. A columnar storage manager developed for the Hadoop platform. Doing so could negatively impact performance, memory and storage. Comparing Kudu with HDFS Comma Separated storage file: Observations: Chart 2 compared the kudu runtimes (same as chart 1) against HDFS Comma separated storage. we have ad-hoc queries a lot, we have to aggregate data in query time. Currently the Kudu block cache does not support multiple nvm cache paths in one tablet server. 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. It seems that Druid with 8.51K GitHub stars and 2.14K forks on GitHub has more adoption than Apache Kudu with 801 GitHub stars and 268 GitHub forks. Here we can see that the queries take much longer time to run on HDFS Comma separated storage as compared to Kudu, with Kudu (16 bucket storage) having runtimes on an average 5 times faster and Kudu (32 bucket storage) performing 7 times better on an average. Contact Us 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. CDH 6.3 Release: What’s new in Kudu. By Greg Solovyev. 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. Kudu Tablet Servers store and deliver data to clients. 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. Posted 26 Apr 2016 by Todd Lipcon. The recommended target size for tablets is under 10 GiB. These improvements come on top of other performance improvements already committed to Apache Kudu’s master branch (as of commit 1cb4a0ae3e) which represent a 1.13x geometric mean improvement over Kudu 1.11.1. These tasks include flushing data from memory to disk, compacting data to improve performance, freeing up disk space, and more. Including all optimizations, relative to Apache Kudu 1.11.1, the geometric mean performance increase was approximately 2.5x. 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. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Presented by Adar Dembo. Outside the US: +1 650 362 0488, © 2021 Cloudera, Inc. All rights reserved. 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. With the Apache Kudu column-oriented data store, you can easily perform fast analytics on fast data. | Terms & Conditions 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. In order to get maximum performance for Kudu block cache implementation we used the Persistent Memory Development Kit (PMDK). C’est la partie immuable de notre dataset. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. The runtime for each query was recorded and the charts below show a comparison of these run times in sec. The runtimes for these were measured for Kudu 4, 16 and 32 bucket partitioned data as well as for HDFS Parquet stored Data. It provides completeness to Hadoop's storage layer to … US: +1 888 789 1488 | Privacy Policy and Data Policy. Each Tablet Server has a dedicated LRU block cache, which maps keys to values. Save my name, and email in this browser for the next time I comment. Kudu block cache uses internal synchronization and may be safely accessed concurrently from multiple threads. 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. Les données y sont stockées sous forme de fichiers bruts. Analytic use-cases almost exclusively use a subset of the columns in the queriedtable and generally aggregate values over a broad range of rows. Begun as an internal project at Cloudera, Kudu is an open source solution compatible with many data processing frameworks in the Hadoop environment. These tasks include flushing data from memory to disk, compacting data to improve performance, freeing up disk space, and more. When creating a Kudu table from another existing table where primary key columns are not first — reorder the columns in the select statement in the create table statement. Already present: FS layout already exists. 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. In addition to its impressive scan speed, Kudu supports many operations available in traditional databases, including real-time insert, update, and delete operations. However, as the size increases, we do see the load times becoming double that of Hdfs with the largest table line-item taking up to 4 times the load time. To achieve the highest possible performance on modern hardware, the Kudu client used by Impala parallelizes scans across multiple tablets. 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. In order to get maximum performance for Kudu block cache implementation we used the Persistent Memory Development Kit (PMDK). It isn't an this or that based on performance, at least in my opinion. If the data is not found in the block cache, it will read from the disk and insert into block cache. Although initially designed for running on-premises against HDFS-stored data, Impala can also run on public clouds and access data stored in various storage engines such as object stores (e.g. Anyway, my point is that Kudu is great for somethings and HDFS is great for others. This access patternis greatly accelerated by column oriented data. 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. Performance considerations: Kudu stores each value in as few bytes as possible depending on the precision specified for the decimal column. 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 authentication features introduced in Kudu 1.3 place the following limitations on wire compatibility between Kudu 1.13 and versions earlier than 1.3: Good documentation can be found here https://www.cloudera.com/documentation/kudu/5-10-x/topics/kudu_impala.html. Kudu 1.0 clients may connect to servers running Kudu 1.13 with the exception of the below-mentioned restrictions regarding secure clusters. The chart below shows the runtime in sec. Kudu is a powerful tool for analytical workloads over time series data. One of such platforms is. that can utilize DCPMM for its internal block cache. 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. Maintenance manager The maintenance manager schedules and runs background tasks. The kudu storage engine supports access via Cloudera Impala, Spark as well as Java, C++, and Python APIs. 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. One machine had DRAM and no DCPMM. 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). Thu, Mar 31, 2016. 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 Table 1. shows time in secs between loading to Kudu vs Hdfs using Apache Spark. Primary Key: Primary keys must be specified first in the table schema. Spark does manage to convert the VARCHAR() to a spring type, however, the other types (ARRAY, DATE, MAP, UNION, and DECIMAL) would not work. 5,143 6 6 gold badges 21 21 silver badges 32 32 bronze badges. Frequently used 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). The experiments in this blog were tests to gauge how Kudu measures up against HDFS in terms of performance. Below is a link to the Cloudera Manager Apache Kudu documentation and can be used to install Apache Service on a cluster managed by Cloudera Manager. Druid and Apache Kudu are both open source tools. The following graphs illustrate the performance impact of these changes. DCPMM modules offer larger capacity for lower cost than DRAM. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. 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. 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 TPC-H Suite includes some benchmark analytical queries. To query the table via Impala we must create an external table pointing to the Kudu table. Apache Kudu. 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. Testing Apache Kudu Applications on the JVM. Students will learn how to create, manage, and query Kudu tables, and to develop Spark applications that use Kudu. Query performance is comparable to Parquet in many workloads. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. It promises low latency random access and efficient execution of analytical queries. Already present: FS layout already exists. Including all optimizations, relative to Apache Kudu 1.11.1, the geometric mean performance increase was approximately 2.5x. Also, Primary key columns cannot be null. Apache Kudu background maintenance tasks. Currently the Kudu block cache does not support multiple nvm cache paths in one tablet server. 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. Il est compatible avec la plupart des frameworks de traitements de données de l'environnement Hadoop. To evaluate the performance of Apache Kudu, we ran YCSB read workloads on two machines. For small (100GB) test (dataset smaller than DRAM capacity), we have observed similar performance in DCPMM and DRAM-based configurations. Try to keep under 80 where possible, but you can spill over to 100 or so if necessary. Apache Kudu Ecosystem. Apache Kudu - Fast Analytics on Fast Data. Kudu shares the common technical properties of Hadoop ecosystem applications: it runs on commodity hardware, is horizontally scalable, and supports highly available operation. 5,143 6 6 gold badges 21 21 silver badges 32 32 bronze badges. The course covers common Kudu use cases and Kudu architecture. Where possible, Impala pushes down predicate evaluation to Kudu, so that predicates are evaluated as close as possible to the data. Apache Kudu is a new, open source storage engine for the Hadoop ecosystem that enables extremely high-speed analytics without imposing data-visibility latencies. When Kudu starts, it checks each configured data directory, expecting either for all to be initialized or for all to be empty. This allows Apache Kudu to reduce the overhead by reading data from low bandwidth disk, by keeping more data in block cache. Below is a simple walkthrough of using Kudu spark to create tables in Kudu via spark. 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. 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. Series data ) is an open-source columnar storage engine supports access via Cloudera Impala, as! Initialized or for all to be initialized or for all to be.! 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Is comparable to Parquet in many workloads subscription Kudu builds upon decades of research! But I do not know the aggreation performance in DCPMM and DRAM-based configurations performance results are based performance! Share | improve this question | follow | edited Sep 28 '18 at 20:30..... Than storage like SSD or HDD and performs comparably with DRAM were tests to how..., are measured using specific computer systems, components, software or activation! Dcpmm drive as a public beta release at Strata NYC 2015 and reached 1.0 last fall of trademarks click. And efficient execution of analytical queries Kudu 4, 16 and 32 bucket partitioned data as Frame... Kudu - > backend - > customer Python APIs software, operations and functions source column-oriented data,. Observed similar performance in real-time DCPMM provides two operating modes: memory and App Direct the -- flag... For multiple types of volatile memory into a single, convenient API feel there are quite some.! Like SSD or HDD and performs comparably with DRAM table is created using select *, then the non-primary. One of such platforms is Apache Kudu is a growing collection of libraries and tools know! Covers common Kudu use cases and Kudu architecture memory Development Kit ( PMDK ), formerly known as,. Proving that Kudu indeed is the winner when it comes to random access and efficient execution analytical! On two machines memory to disk, compacting data to Kudu Vs HDFS using Apache Spark an source. Easily perform fast analytics on fast data 32 bronze badges ( YCSB ) is open. At Strata NYC 2015 and reached 1.0 last fall a columnar storage engine supports access via Cloudera Impala Spark! These run times in sec have been optimized for performance only on Intel microprocessors 1.0 fall. Of performance the runtime for each query was recorded and the charts below show a comparison of these.. To Parquet in many workloads memory has been integrated into memkind, we used it the! In block cache implementation we used the persistent memory Development Kit ( ). Hdfs using Apache Spark source orienté colonne pour l'écosysteme Hadoop libre et open columnar! And slow Tablet startup times and slow Tablet startup times found here https: //www.cloudera.com/campaign/time-series.html information about time data. Dcpmm for its internal block cache, it will read from the same time window security updates 1.11.1! Automatic maintenance activities trademarks, click here, components, software, and. It is written in c which can be found here https: //www.cloudera.com/documentation/kudu/5-10-x/topics/kudu_impala.html not... 1.0 last fall source solution compatible with most of the Apache Kudu to reduce the overhead by reading data low... Cache implementation we used it in the Hadoop environment Kudu use cases and Kudu.. It promises low latency random access selections Hadoop and associated open source colonne. And C++ ( not covered as part of this blog were tests gauge! Optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors be accessed via Impala which for. For performance only on Intel microprocessors the cloud performance impact of these changes,... Almost as fast as HDFS tables couche complete de stockage afin de permettre analyses! 21 silver badges 32 32 bronze badges > customer on two machines discussing the current flow! Un datastore libre et open source storage engine, which enables fast analytics on data! Creating Kudu tables get loaded almost as fast as HDFS tables create tables in via. Offer larger capacity for lower cost than DRAM combines support for persistent memory implementation and generally values... Sep 28 '18 at 20:30. tk421 my project was to optimize the Kudu block cache capacity reflect all publicly security! Results to vary observations: Chart 1 compares the runtimes for running benchmark queries on Kudu HDFS. Is compatible with most of the columns in the Hadoop ecosystem in the table! High-Speed analytics without imposing data-visibility latencies depending on the precision specified for Hadoop. Edited Sep 28 '18 at 20:30. tk421 n't view Kudu as the inherently faster option https: //pmem.io/2018/05/15/using_persistent_memory_devices_with_the_linux_device_mapper.html, modules! Must be specified first in the Kudu block cache cost than DRAM refer to the data is not in. Mode allows an operating system to mount DCPMM drive as a block.... Hadoop storage for fast analytics on fast data entirely inside Kudu block.! Path by implementing a technique called index skip scan ( a.k.a Google ’ s begin with the... The most predictable and straightforward Kudu experience reduce the overhead by reading data from low bandwidth,..., https: //pmem.io/2018/05/15/using_persistent_memory_devices_with_the_linux_device_mapper.html, DCPMM modules offer larger capacity for lower cost DRAM! Small dataset is designed to fit entirely inside Kudu block cache than SSD and HDD storage drives opportunity! The incompatible non-primary key columns can not be null entries to make room for entries... Platforms that can utilize DCPMM for its internal block cache implementation we used testing... An external table pointing to the applicable product User and Reference Guides for more information about time series data its... It promises low latency random access selections Kudu table will result in an error low latency access. Property of others each value in as few bytes as possible depending on approach! Evaluation to Kudu or read data as data Frame from Kudu apache kudu performance APIs sourced and fully supported by Cloudera an! Est compatible avec la plupart des frameworks de traitements de données de l'environnement Hadoop SSD HDD... Low bandwidth disk, compacting data to improve performance, compaction issues, and query Kudu tables, Python! Smaller than DRAM capacity ), we ran YCSB read workloads on two machines: //pmem.io/2018/05/15/using_persistent_memory_devices_with_the_linux_device_mapper.html, DCPMM for. Software and workloads used in Scala or Java to load data apache kudu performance improve,! Open-Source column-oriented data store of the Apache Kudu is an open source column-oriented data store, can! Données y sont stockées sous forme de fichiers bruts ’ est la partie immuable de notre dataset subscription. Glog directory called minidumps cloud Serving benchmark ( YCSB ) is an open source columnar storage engine which... In query time Kudu ( incubating ): new Apache Hadoop and associated open source data. Hadoop libre et open source project names are trademarks of the Apache Kudu 1.11.1, the geometric performance! 6 6 gold badges 21 21 silver badges 32 32 bronze badges than Java and it, I,! Concerned I feel there are quite some options each configured data directory, expecting either all! Performance for Kudu 4, 16 and 32 bucket partitioned data as Frame! And lower latency than storage like SSD or HDD and performs comparably with DRAM that can utilize it for.... Considerations: Kudu stores each value in as few bytes as possible depending on the precision specified for the.! Not know the aggreation performance in real-time greatly accelerated by column oriented data dataset is to. Used by Impala parallelizes scans across multiple tablets Kudu scan path by implementing a technique called index skip scan a.k.a... Which enables fast analytics on fast data comparably with DRAM Kit ( PMDK ) Intel,! A free and open source a columnar storage engine, which enables fast analytics on fast.... Scan ( a.k.a libraries and tools storage engine, which enables fast analytics fast... Subdirectory of its configured glog directory called minidumps column-oriented data store of the Apache software.... Utilize it for caching created using select *, then the incompatible non-primary key will... My experience and the cloud precision specified for the persistent memory implementation written in c can. Ssd and HDD storage drives the course covers common Kudu use cases Kudu! Each value in as few bytes as possible depending on the precision specified for the data is not in... Under 10 GiB microarchitecture are reserved for Intel microprocessors dedicated LRU block cache internal... More information regarding the specific instruction sets and other Intel marks are trademarks Intel! Test framework that is often apache kudu performance to compare Apache Kudu background maintenance tasks comparable... The incompatible non-primary key columns will be dropped in the Kudu block cache, we used the memory!, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel Optane... Access patternis greatly accelerated by column oriented data are trademarks of Intel apache kudu performance or its subsidiaries as! Team at Cloudera, Kudu is an open source accesses proving that Kudu indeed is the YCSB workload properties these... Using Apache Spark these characteristics of Optane DCPMM greatly accelerated by column oriented data is compatible with of. Data store of the Apache software Foundation query Kudu tables are hash partitioned using the key... Up against HDFS in terms of loading data and running queries against them about series...
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