Three great posts on the Hortonworks’ blog, part 1, part 2, and part 3, detailing
the most important new features included with the Apache Pig 0.9 release:
- embedding: “You can now write a python program and embed Pig scripts inside of it, leveraging all language features provided by Python, including control flow”
- project-range expressions
- improved error messages
- typed maps
- new UDFs
Original title and link: Pig 0.9: New Features Documented ( ©myNoSQL)
From a Teradata PR announcement:
SQL-MapReduce® is a framework which enables fast, investigative analysis of complex information by data scientists and business analysts. It enables procedural expressions in software languages (such as Java, C#, Python, C++, and R) to be parallelized across a group of linked computers (compute cluster) and then activated for use (invoked) with standard SQL.
The closest open source solution I can think of is Pig , created and open sourced by Yahoo! (PDF).
Original title and link: Aster Data SQL-MapReduce Technology Patent ( ©myNoSQL)
Remember when everyone was suggesting solutions for Twitter architecture? Now the Library of Congress is trying to figure out what technologies to use to store the Twitter archive:
The project is still very much under construction, and the team is weighing a number of different open source technologies in order to build out the storage, management and querying of the Twitter archive. While the decision hasn’t been made yet on which tools to use, the library is testing the following in various combinations: Hive, ElasticSearch, Pig, Elephant-bird, HBase, and Hadoop.
Note that in terms of storage only HBase is mentioned—Twitter’s main tweet storage is MySQL though.
Original title and link: Choosing Technologies: The Library of Congress and the Twitter Archive (NoSQL database©myNoSQL)
If you don’t count the download, you’ll get this up and running in 5 minutes tops. At the end you’ll have Hadoop, Sqoop, Pig, Hive, HBase, ZooKeeper, Oozie, Hume, Flume, and Whirr all configured and ready to experiment with.
Making it easy for users to experiment with these tools increases the chances for adoption. Adoption means business.
Original title and link: Experimenting with Hadoop using Cloudera VirtualBox Demo (NoSQL databases © myNoSQL)
Apixio uses Hadoop and Pig for analysing medical records and Cassandra for serving seach queries. All production machines are Amazon EC2 instances.
Bob Rogers, Apixio’s chief scientist, explained the importance of machine learning and unstructured-data analysis in the medical field. He said because of the proliferation of ontologies — area-specific terminology for everything from billing to scan results — any sort of search engine must be able to create degrees of association between the various ontologies, as well as common language.
It sounds like the perfect setup for Brisk.
Original title and link: Apixio Using Hadoop, Pig and Cassandra for Advanced Analytics on Medical Records (NoSQL databases © myNoSQL)
Three presentations covering the various NoSQL usages at Twitter:
Kevin Weil talking about data analysis using Scribe for logging, base analysis with Pig/Hadoop, and specialized data analysis with HBase, Cassandra, and FlockDB on InfoQ
Ryan King’s presentation from last year’s QCon SF NoSQL track on Gizzard, Cassandra, Hadoop, and Redis on InfoQ
Dmitriy Ryaboy on Hadoop from Devoxx 2010:
By looking at the powered by NoSQL page and my records, Twitter seems to be the largest adopter of NoSQL solutions. Here is an updated version of who is using Cassandra and HBase
- Twitter: Cassandra, HBase, Hadoop, Scribe, FlockDB, Redis
- Facebook: Cassandra, HBase, Hadoop, Scribe, Hive
- Netflix: Amazon SimpleDB, Cassandra
- Digg: Cassandra
- SimpleGeo: Cassandra
- StumbleUpon: HBase, OpenTSDB
- Yahoo!: Hadoop, HBase, PNUTS
- Rackspace: Cassandra
And probably many more missing from the list. But that could change if you leave a comment.
Original title and link: Hadoop and NoSQL Databases at Twitter (NoSQL databases © myNoSQL)
New version of Cloudera’s Hadoop distro — complete release notes available here:
CDH3 Beta 4 also includes new versions of many components. Highlights include:
- HBase 0.90.1, including much improved stability and operability.
- Hive 0.7.0rc0, including the beginnings of authorization support, support for multiple databases, and many other new features.
- Pig 0.8.0, including many new features like scalar types, custom partitioners, and improved UDF language support.
- Flume 0.9.3, including support for Windows and improved monitoring capabilities.
- Sqoop 1.2, including improvements to usability and Oracle integration.
- Whirr 0.3, including support for starting HBase clusters on popular cloud platforms.
Plus many scalability improvements contributed by Yahoo!.
Cloudera’s CDH is the most popular Hadoop distro bringing together many components of the Hadoop ecosystem. Yahoo remains the main innovator behind Hadoop.
Original title and link: Cloudera’s Distribution for Apache Hadoop version 3 Beta 4 (NoSQL databases © myNoSQL)
In order to not have to learn everything about setting up Hadoop and still have the ability to leverage the power of Hadoop’s distributed data processing framework and not have to learn how to write map reduce jobs and … (this could go on for a while so I’ll just stop here). For all these reasons, I choose to use Amazon’s Elastic Map infrastructure and Pig.
I will talk you through how I was able to do all this [take my log data stored on S3 (which is in compressed JSON format) and run queries against it] with a little help from the Pig community and a lot of late nights. I will also provide an example Pig script detailing a little about how I deal with my logs (which are admittedly slightly abnormal).
Sadly such an useful tool in the Hadoop ecosystem doesn’t make the headlines.
Original title and link: Pig Latin and JSON on Amazon Elastic Map Reduce (NoSQL databases © myNoSQL)
Since direct integration of data flow and control flow is neither reasonable nor desirable, a heuristic is needed to productively combine the two. […] Compared to an approach that integrates control flow and data flow, such as PL/SQL, embedding in an existing scripting language is a much lower development and maintenance effort. It will also be much easier for users, who will be able to use existing development tools (IDEs, debuggers, etc.) to work with their scripts.
The first proposal—macro expansions— has already been committed and will be included in the next Pig Latin release.
Original title and link: Pig Latin Adds Macros as Part of Becoming Turing Complete (NoSQL databases © myNoSQL)
We currently have nearly 100 people working on Apache Hadoop and related projects, such as Pig, ZooKeeper, Hive, Howl, HBase and Oozie. Over the last 5 years, we’ve invested nearly 300 person-years into these projects. […] Today Yahoo runs on over 40,000 Hadoop machines (>300k cores). They are used by over a thousand regular users from our science and development teams. Hadoop is at the center of our research in search, advertising, spam detection, personalization and many other topics.
I assume there’s no surpise to anyone I’m a big fan of Yahoo! open source initiatives.
Original title and link: The Backstory of Yahoo and Hadoop (NoSQL databases © myNoSQL)
Dmitriy Ryaboy1 has a guest post on Cloudera blog covering the new features in Apache Pig 0.8.
- Support for user defined functions (UDF) in scripting languages
- Generic UDFs: allows invocation of static java methods
- PigUnit: as the name suggests, a testing tool for Pig scripts
- PigStats: once again the name should give you a hint of what it does: better visibility into Pig job through a series of stats, XML-based metadata injected into Map-Reduce jobs, and listeners for the Pig process
- Scalar values: simplifying access to single-row relations
- possibility to start a monitoring thread for long running executions
- HBaseStorage: works with HBase 0.20 releases only
- flow allows custom Map-Reduce jobs
- automatic merge of small files
- custom partitioners
The Pig 0.8 release includes a large number of bug fixes and optimizations, but at the core it is a feature release. It’s been in the works for almost a full year and the amount of time spent on 0.8 really shows.
You can also check Dmitriy’s presentations about the NoSQL ecosystem at Twitter: Twitter, Pig, and HBase and HBase and Pig: The Hadoop ecosystem at Twitter
Original title and link: Apache Pig 0.8: What is New (NoSQL databases © myNoSQL)
Rajarshi Guha about Pig Latin:
While the implementation of such code [SMARTS matching and pharmacophore searching] is pretty straightforward, it’s still pretty heavyweight compared to say, performing SMARTS matching in a database via SQL. On the other hand, being able to perform these tasks in Pig Latin, lets us write much simpler code that can be integrated with other non-cheminformatics code in a flexible manner.
Extensibility over compactness.
Original title and link: Pig and Cheminformatics (NoSQL databases © myNoSQL)