hadoop parquet 11 New Thoughts About Hadoop Parquet That Will Turn Your World Upside Down
Last week, I wrote briefly about Apache HBase blame out its 1.0 release. Subsequently, account of several added new releases of Big Data-related projects formed out of the Apache Software Foundation. I anticipate it’s important to booty banal of these releases, beneath for the account of alive the capacity of each, and added for the purpose of acute some absorbing trends in the greater Hadoop ecosystem overall.
The new releases accommodate the accompanying 5.0.0 releases of chase activity Apache Lucene and its Solr sub-project; the 2.3 absolution of the Apache Parquet cavalcade abundance book architectonics (which is still an incubator project); a rather asymptotically-numbered 1.99.5 absolution of Apache Sqoop, which offers an absorption band about MapReduce for affective abstracts amid HDFS and assorted abstracts barn platforms; and the 0.9.4 absolution of an absorbing Apache incubator activity alleged MRQL.
Solr powerLucene and Solr accept been about for a continued time and Lucene has an important accord with Hadoop: Cloudera Chief Architect Doug Cutting is the architect of both. Beyond that, Cloudera, Hortonworks and MapR accommodate a chase interface for Hadoop that is congenital aloft Solr and Lucene and can basis abstracts stored in HDFS – Hadoop’s book system.
The 5.0.0 releases accept a cardinal achievement and architectonics enhancements that calm assume to aggregate a through abode charwoman and addition of the platform. As Lucene becomes anchored in anytime added product, projects and engines, this is acceptable to see.
Smooth…creamy…butter!Speaking of book systems, and of Cloudera, the Apache Parquet book format, which allows for accumulator of cavalcade abundance abstracts in simple files, is growing in importance. Announced about two years ago, back it was a collective activity amid Cloudera and Twitter, the Parquet architectonics became an Apache Incubator activity this accomplished May. Parquet is advised accurately for Hadoop, and finer serves as Impala’s built-in book format. But added Apache projects are accordant with it, including Hive, Pig and Drill.
As Parquet has Cloudera’s banner on it, we apparently shouldn’t be too afraid that addition cavalcade abundance book format, alleged ORC (Optimized Row Columnar), is out there as well, and has been abundantly apprenticed by Hortonworks. ORC is about Hive’s built-in book format, afterwards the RCFile (Record Columnar file) architectonics in that role. There are a lot of commonalities amid Parquet and ORC, and aggregation of the two, admitting unlikely, wouldn’t be the affliction affair for the ecosystem.
Two Sqoops of dataApache Sqoop (a abbreviating of SQL-to-Hadoop) is an absorbing animal. At a time back Hadoop is affective abroad from MapReduce and abstracts movement is more advised article to be avoided, Sqoop continues as a MapReduce-based abstracts movement tool. At adaptation 1.99.5, it would assume that we’re about accessible to acceptable in the era of “Sqoop 2.0.” But the Sqoop2 project, which adds a UI and greater accordance to Sqoop, was kicked off over three years ago and yet the Sqoop Web armpit says that Sqoop 1.99.5 isn’t feature-complete or advised for assembly deployment. So, as with the beheading of aggregate abstracts transfers, it looks like we’ll charge to bustle up and wait.
I accept in MRQLThe final Apache Big Abstracts activity to adviser in a new absolution is MRQL (pronounced “miracle”), which provides a SQL absorption band over several altered broadcast accretion platforms, including Hadoop MapReduce and Apache Spark, which many are accustomed with, as able-bodied as Apache Hama and Apache Flink, with which I cartel say abounding are not.
I will accept absolutely audibly that I am not abstruse in the means of MRQL, but I acquisition some things absorbing about the engines on which it can run:
The take-away? YARN is at the centermost of a lot of things. But so is Spark. Spark doesn’t crave YARN or Hadoop, and neither does Hama, MRQL or annihilation that can run on either of them. Could YARN ironically facilitate a accident of drive for Hadoop, as it brings cluster-type-agnostic engines into the Hadoop amphitheater which again woo users out of that amphitheater because of their own attraction and self-sufficiency?
Big Data: structureAfter accessory the Strata Hadoop World appointment in San Jose a brace of weeks ago, my Gigaom Account colleague, Derrick Harris, opined that for now, Spark looks like the approaching of Big Data. I’m beneath convinced, but as we arch into Gigaom’s own Structure: Abstracts appointment in New York the anniversary afterwards next, I’ll be giving this a lot of thought, abnormally while I accept the amusement of interviewing Spark co-creator and Databricks CEO Ion Stoica on date at the event.
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