Apache Phoenix

1
What Is Apache Phoenix ?

• Massively parallel, relational database engine
• Supports OLTP for Hadoop
• Uses Apache HBase as its backing store
• Open source / Apache 2.0 license
• Written in Java , SQL
• ACID (atomicity, consistency, isolation,
  durability)
• Via Apache Tephra integration

2
Phoenix SQL Support

• Accepts SQL queries
• Compiles them to HBase scans
• Orchestrates running of scans
• Produces regular JDBC result sets
• Creates performance gains by using
• HBase API/coprocessors/custom filters
• Results in query response times
• Milliseconds for small queries
• Seconds for tens of millions of rows

3
Phoenix SQL Support

• See phoenix.apache.org for full syntax support

4
Phoenix Environment
5
Phoenix Bulk Loading

• Bulk load data via
• Single-threaded for CSV via psql i.e.
• bin/psql.py -t EXAMPLE localhost data.csv
• Load for EXAMPLE table
• For HBase on local machine
• MapReduce-based for CSV and JSON
• See next slide

6
Phoenix Bulk Loading

• Bulk load example for MapReduce
• For CSV and JSON loads
• Using Phoenix MapReduce library
• Against the EXAMPLE table

7
Phoenix Performance
8
Phoenix User-defined functions(UDFs)

• Create temporary/permanent UDF's
• Temporary for session only
• Use UDF's in SQL and Indexes
• Permanent UDF's stored in SYSTEM.FUNCTION
• Tenant specific UDF usage supported
• UDF jar files must be placed on HDFS
• UDF jar updates not currently possible
• (without cluster bounce)

9
Phoenix Transactions

• Cross row/table/ACID support using Apache Tephra
• Transactional functionality currently beta
• Enable transactions and snapshot dir in
  hbase-site.xml
• Also set a transational timeout value
• Start Tephra
• Create tables with flag TRANSACTIONALtrue
• Then transactions act as follows
• Start with statement against table
• End with commit or rollback

10
Available Books

• See Big Data Made Easy
• Apress Jan 2015
• See Mastering Apache Spark
• Packt Oct 2015
• See Complete Guide to Open Source Big Data
  Stack
• Apress Jan 2018
• Find the author on Amazon
• www.amazon.com/Michael-Frampton/e/B00NIQDOOM/
• Connect on LinkedIn
• www.linkedin.com/in/mike-frampton-38563020

11
Connect

• Feel free to connect on LinkedIn
• www.linkedin.com/in/mike-frampton-38563020
• See my open source blog at
• open-source-systems.blogspot.com/
• I am always interested in
• New technology
• Opportunities
• Technology based issues
• Big data integration