In my JavaOne talk last week, I presented changes that are happening in Hadoop: It’s shaking off it’s batch-based shackles and enabling a new Hadoop platform that can support a mix of processing systems, from stream-processing systems to NoSQL systems.
The slides for my talk can be viewed on Speaker Deck. The rest of this post is an overview of the technologies covered in my talk, along with links for further reading.
With Hadoop 2.x, we now have YARN, which acts as a distributed scheduler. This is a big step towards the vision of Hadoop being the Big Data Kernel, as it allows arbitrary applications to be scheduled on the same Hadoop cluster, and enables a new world where we can have silo’d applications coexisting on the same hardware and sharing the same storage.
The following links serve as a good starting ground to learn more about YARN:
- An introduction to YARN: http://hortonworks.com/blog/introducing-apache-hadoop-yarn/
- A book by Arun Murthy et. al. on YARN: http://www.amazon.com/Apache-Hadoop-YARN-Processing-Addison-Wesley/dp/0321934504, first chapter can be read for free at http://hortonworks.com/wp-content/uploads/downloads/2013/06/Apache.Hadoop.YARN_.Sample.pdf.
- The YARN ResourceManager: http://hortonworks.com/blog/apache-hadoop-yarn-resourcemanager/
- Writing YARN applications: http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/WritingYarnApplications.html
- Setting up a cluster to run MapReduce on YARN: http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/SingleCluster.html
- Configuring YARN: http://hortonworks.com/blog/how-to-plan-and-configure-yarn-in-hdp-2-0/
- Default YARN configuration: http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-common/yarn-default.xml
- YARN commands: http://archive.cloudera.com/cdh4/cdh/4/hadoop/hadoop-yarn/hadoop-yarn-site/YarnCommands.html
HBase is a NoSQL, distributed multi-dimensional map based on Google’s BigTable. It uses HDFS for persistence, which is a huge benefit if a key requirement of your NoSQL system is the ability to read and write data into HBase using MapReduce.
- HBase project page: http://hbase.apache.org/ and mailing lists: http://hbase.apache.org/mail-lists.html
- A good presentation by Amandeep Khurana on HBase: http://www.slideshare.net/amansk/hbase-hadoop-day-seattle-4987041
- HBase wiki: http://wiki.apache.org/hadoop/Hbase
- The HBase Reference Guide - a great resource on how HBase’s data model, design and configuration: http://hbase.apache.org/book.html
- HBase in Action, a book from Manning: http://www.manning.com/dimidukkhurana/
HBase on YARN (Hoya)
Hoya is a YARN application that allows multiple HBase clusters to coexist on a single Hadoop YARN cluster. This provides strong data/resource isolation properties, in conjunction with the ability to easily spin up, upsize/downsize and shutdown HBase clusters. Hoya was developed by Steve Loghran and friends over at Hortonworks.
- GitHub project: https://github.com/hortonworks/hoya/
- Introducing Hoya: http://hortonworks.com/blog/introducing-hoya-hbase-on-yarn/
- Hoya architecture: http://hortonworks.com/blog/hoya-hbase-on-yarn-application-architecture/
- Presentation by Steve and Devaraj: http://www.slideshare.net/steve_l/hoya-hbase-on-yarn-20130820-hbase-hug
Accumulo is a BigTable implementation much like HBase. It also uses HDFS for storage, and currently has an edge in the security world due to its cell-level security. Although it should be noted that this is planned for HBase (see HBASE-6222).
- Project page: http://accumulo.apache.org/
- Todd Lipcon’s presentation comparing HBase and Accumulo http://www.slideshare.net/cloudera/h-base-and-accumulo-todd-lipcom-jan-25-2012
ElephantDB is a read-only key-value store, which uses HDFS to load data, which is served in real-time. It’s a part of Nathan Marz’s Lambda Architecture and enables the rapid loading and serving of data produced in the batch tier.
- GitHub page: https://github.com/nathanmarz/elephantdb
- Presentation by Nathan Marz: http://www.slideshare.net/nathanmarz/elephantdb
- Presentation by Soren Macbeth, a contributor to the project: https://speakerdeck.com/sorenmacbeth/introduction-to-elephantdb
Storm is a stream processing, continuous computation and distributed RPC system developed and open-sourced by Twitter. It allows you to perform near real-time calculations such as trending topics.
- Project home: http://storm-project.net/
- GitHub project: https://github.com/nathanmarz/storm
- Extensive documentation which covers the background and basics on how Storm works: https://github.com/nathanmarz/storm/wiki
- Natan Marz presentation on Storm: http://www.youtube.com/watch?v=bdps8tE0gYo
- Running a multi-node Storm cluster from Michael Noll: http://www.michael-noll.com/tutorials/running-multi-node-storm-cluster/
- Understanding the parallelism of a Storm topology, also from Mr. Noll: http://www.michael-noll.com/blog/2012/10/16/understanding-the-parallelism-of-a-storm-topology/
Storm on YARN
Yahoo uses Storm for a variety of use cases, and created the Storm-on-YARN so that they could run Storm on their YARN clusters. They also added the ability for Storm to read/write to secure HDFS.
- GitHub project page: https://github.com/yahoo/storm-yarn
- Yahoo! blog post introducing the project: http://developer.yahoo.com/blogs/ydn/storm-yarn-released-open-source-143745133.html
- Hortonworks blog on the project: http://hortonworks.com/blog/streaming-in-hadoop-yahoo-release-storm-yarn/
- Hadoop Summit 2013 presentation: http://www.slideshare.net/Hadoop_Summit/feng-june26-1120amhall1v2
Samza (incubating) is a stream processing system that uses Kafka for messaging, and optionally YARN for resource management.
- Project page: http://samza.incubator.apache.org/
- LinkedIn post on Samza’s background: http://engineering.linkedin.com/data-streams/apache-samza-linkedins-real-time-stream-processing-framework
Morphlines is an ETL library from Cloudera that has implementations available for use within Flume, MapReduce and HBase. Using a modified JSON syntax, it allows you to create a pipeline of work which can fulfill use cases such as near real-time writes from Flume into Solr Cloud.
- GitHub page: https://github.com/cloudera/search
- Introductory blog post: http://blog.cloudera.com/blog/2013/07/morphlines-the-easy-way-to-build-and-integrate-etl-apps-for-apache-hadoop/
- Presentation from Cloudera: http://www.slideshare.net/cloudera/using-morphlines-for-onthefly-etl
- Documentation as part of the Cloudera Development Kit: http://cloudera.github.io/cdk/docs/0.5.0/cdk-morphlines/index.html
Giraph is a framework for performing offline batch processing of semi-structured graph data on a massive scale. It offers performance advantages over graph processing with MapReduce.
- Project page: http://giraph.apache.org/
- Quick start guide: http://giraph.apache.org/quick_start.html
- HadoopSummit 2013 presentation: http://www.youtube.com/watch?v=_RsJfZGQo9I
- Architectural overview: http://www.slideshare.net/averyching/20111014hortonworks
Impala from Cloudera is an implementation of Google’s paper on Dremel, and provides interactive SQL capabilities on top of data in HDFS and HBase.
- GitHub page: https://github.com/cloudera/impala
- Project announcement from Cloudera: http://blog.cloudera.com/blog/2012/10/cloudera-impala-real-time-queries-in-apache-hadoop-for-real/
- Impala 1.0 release announcement: http://blog.cloudera.com/blog/2013/05/cloudera-impala-1-0-its-here-its-real-its-already-the-standard-for-sql-on-hadoop/
- Configuring Impala for multi-tenant performance: http://blog.cloudera.com/blog/2013/06/configuring-impala-and-mapreduce-for-multi-tenant-performance/
- Cloudera presentation at the Swiss Big Data User Group: http://www.slideshare.net/SwissHUG/cloudera-impala-15376625
An (incubating) project that offers the promise of interactive SQL capabilities over data in HDFS, HBase, Cassandra, MongoDB and Splunk.
- Apache incubating project page: http://incubator.apache.org/drill/
- Architecture outlines: http://www.slideshare.net/jasonfrantz/drill-architecture-20120913
Parquet, a joint initiative from Cloudera and Twitter, is a columnar data format supporting nested data. It can offer space and time advantages over row-ordered data, especially with queries that return a subset of the overall columns. It supports a wide variety of tools (MapReduce, Impala, Pig and Hive) and is used in production by Twitter.
- GitHub page: https://github.com/Parquet
- Presentation from Cloudera Impala meetup: http://www.slideshare.net/cloudera/presentations-25757981
- Hadoop Summit 2013 presentation: http://www.youtube.com/watch?v=pFS-FScophU and accompanying slides http://www.slideshare.net/julienledem/parquet-hadoop-summit-2013
- Twitter blog post: https://blog.twitter.com/2013/dremel-made-simple-with-parquet
- Cloudera blog post: http://blog.cloudera.com/blog/2013/03/introducing-parquet-columnar-storage-for-apache-hadoop/
ORC File is a columnar data format that also supports nested data. It is currently implemented within Hive 0.11.
- Presentation from Hortonworks: http://www.slideshare.net/oom65/orc-files
- Details on the file format: https://cwiki.apache.org/Hive/languagemanual-orc.html
- Hadoop Summit 2013 presentation http://www.youtube.com/watch?v=GV7vpR7vpjM and slides http://www.slideshare.net/oom65/orc-andvectorizationhadoopsummit
Tez (incubating) is a generalized DAG execution engine. The goal of the project is to remove disk barriers that exist with pipelined MapReduce jobs. The first goal of the project is to provide a MapReduce implementation using Tez, followed by Hive and Pig.
- Incubating page at Apache: http://incubator.apache.org/projects/tez.html
- Introducing Tez: http://hortonworks.com/blog/introducing-tez-faster-hadoop-processing/
- Hadoop Summit 2013 presentation http://www.youtube.com/watch?v=9ZLLzlsz7h8 and accompanying slides http://www.slideshare.net/Hadoop_Summit/murhty-saha-june26255pmroom212
Mesos is a cluster manager, similar to YARN, providing resource sharing and isolation capabilities in a distributed cluster. It can support multiple instances and versions of Hadoop, Spark and other applications. It’s used in Twitter to manage various applications in production.
The Lambda Architecture, an architectural blueprint from Nathan Marz, suggests that speed and batch layers should exist to play to their mutual strengths: the speed layer providing near real-time data aggregations, and the batch layer providing a mechanism to correct potential mistakes made in the speed layer.
- Nathan’s book, Big Data from Manning, which goes into detail on the Lambda Architecture: http://www.manning.com/marz/
- Nathan’s presentation explaining the background behind Lambda: http://www.slideshare.net/nathanmarz/runaway-complexity-in-big-data-and-a-plan-to-stop-it
Summingbird is a project out of Twitter which could be viewed as an implementation of the Lambda Architecture. It allows you to using a single API to define operations on distributed collections which can be mapped into MapReduce or Storm executions.
- GitHub project page: https://github.com/twitter/summingbird
- Twitter blog post on Summingbird: https://blog.twitter.com/2013/streaming-mapreduce-with-summingbird
- Sam Ritchie presentation on Summingbird: http://www.youtube.com/watch?v=Y3PETLJeP7o and accompanying slides https://speakerdeck.com/sritchie/summingbird-streaming-mapreduce-at-twitter
Spark (incubating) is an in-memory distributed processing system which allows you to perform MapReduce, as well as iterative workloads over data. Spark and its family of associated projects (such as Spark Streaming, GraphX) offers a complete solution to most distributed processing use cases.
- Project page: http://spark.incubator.apache.org/
- Documentation, including links to video tutorials: http://spark.incubator.apache.org/documentation.html