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Low latency SQL querying on HBase

HBase has emerged as one of the most popular NoSQL database offering distributed, versioned, non-relational tables hosted on commodity hardware. However, with a large set of users coming from a relational SQL world, it made sense to bring the SQL back in this NoSQL. With Apache Phoenix, database professionals get a convenient way to query HBase through SQL in a fast and efficient manner. Continuing our discussion with James Taylor, the founder of Apache Phoenix, we focus on the functional aspects of Phoenix in this second part of interaction.

Although Apache Phoenix started off with distinct low latency advantage, have the other options like Hive/Impala (integrated with HBase) caught up in terms of performance?


No, these other tools such as Hive and Impala have not invested in improving performance against HBase data, so if anything, Phoenix's advantage has only gotten bigger as our performance improves. 
See this link for comparison of Apache Phoenix with Apache Hive and Cloudera Impala.

Apache Phoenix and Cloudera Impala comparison
(Query: select count(1) from table over 1M and 5M rows)

What lies ahead on roadmap for Apache Phoenix in 2015?


Our upcoming 4.4 release introduces a number of new features:  User Defined Functions, UNION ALL support, Spark integration, Query Server to support thin (and eventually non Java) clients, Pherf tool for testing at scale, MR-based index population, and support for HBase 1.0.

We are also actively working on transaction support by integrating with Tephra (http://tephra.io/). If all goes according to plan, we'll release this after our 4.4 release (in 4.5 or 5.0), as this work is pretty far along (check out our txn branch to play around with it).

In parallel with this, we're working on Apache Calcite integration to improve interop with the greater Hadoop ecosystem through plugging into a rich cost-based optimizer framework. IMHO, this is the answer to ubiquitous usage of Phoenix for HBase data across queries that get data from any other Calcite adapter source (RDBMS, Hive, Drill, Kylin, etc.). This will allow a kind of plug and play approach with this the push down being decided based on a common cost model that all these other tools plug into.

Come hear more and see a demo at our upcoming Meetups or at HBaseCon 2015. 


Does Apache Phoenix also talk to HCatalog or is that interaction left off to HBase itself?


Phoenix manages its metadata through a series of internal HBase tables. It has no interaction with HCatalog.


Can Apache Phoenix be connected with BI tools which have traditionally relied on ODBC drivers?


Phoenix can connect with BI tools that support a JDBC driver. However, BI tools that rely on an ODBC driver are more challenging. There's a new thin driver plus query server model that we support in our upcoming 4.4 release which will help, though. This thin driver will open the door for an ODBC driver to be achievable by writing the same protocol that our Java-based thin driver use (JSON over http).


Which commercial distributions is Apache Phoenix part of? 


Apache Phoenix is available in the Hortonworks HDP distribution. Make sure to let your vendor of choice know that you'd like to see Phoenix included in their distribution as well, as that's what will make it happen.


James Taylor is an architect at salesforce.com in the Big Data Group. He founded the Apache Phoenix project and leads its on-going development efforts. Prior to Salesforce, James worked at BEA Systems on projects such as a federated query processing system and a SQL-based complex event programming platform, and has worked in the computer industry for the past 20+ years at various start-ups. He lives with his wife and two daughters in San Francisco.

Comments

  1. Apache phoenix performs poorly with large dataset, say 15 millions rows. Even a simple "select count(*) from ABC" query takes 52 seconds. Resultset iterator is very slow.

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