Apache Phoenix

Created: by Pradeep Gowda Updated: Feb 20, 2018


Where does Phoenix fit in?

phoenix demo

  • 10 years of Fortune 500 stocks
  • 30M rows
  • Single AWS node
  • interactive visual query for stock prices …

What is Hbase

  • Runs on HDFS
  • K/V store – distributed, sorted, map
  • low level APIs – get, put values given row key & scan over range given start and stop row keys.

Why use HBase?

  • you have lots of data. scale linearly. shards automatically. data is split and redistributed
  • no transactions out of the box.
  • you need strict consistency.
  • hbase handle incremental data changes by building log structure merge trees

Why use Phoenix?

  • SQL

  • reduces the amount of code users need to write.

  • Performance optimisations transparent to user

    • aggregation
    • skip scanning
    • secondary indexing
    • query optimisation
  • Leverage existing tooling around SQL

    • SQL Client (bin/sqlline.py)
    • OLAP engine. eg: Pentaho. Kylin?

Phoenix versus Hive performance

  • Phoenix (key filter) >> phoenix (full table) > >> Hive over HBase
  • Hive and Impala have similar perf. Does not understand the structure of row-keys

Use cases

  • Data archival

    • from oracle into HBase while maintaining salesforce developer API
  • Platform monitoring

Why is it important?

  • scalable, low latency app development starts with Phoenix
  • Phoenix worries about physical scale and fast performance so that app developers don’t have to
  • Looks, tastes, feels like SOQL to force.com developer

Archival problem set

  • Field history tracking grows unbounded. call centers call individual change. No data is deleted for 8.5 years.
  • Enterprise customers require long term storage of ‘cold’ data.
  • Data retention policies can require of data to be kept around

Archive pilot demonstration

  • insret data from Oracle into Phoenix

Monitoring use case

  • security, compliance and audit
  • product support and “Limits analysis”
  • product usage and management (roll up into aggregates)
  • Identity fraud

Phoenix under the hood

  • aggregate co-processor on severside per region, parallelised.
  • returns only the distinct set of features
  • preserves network bandwidth
  • client does final merge-sort.
  • client returns the result as a JDBC result

Roadmap (Nov, 2013)

  • Apache incubator
  • Joins
  • multi-tenant tables
  • monitoring and manageent
  • cost-based query optimizer
  • sql OLAP extensions (WINDOW, PARTITION OVER, RANK)
  • Transactions

State of the union (Jun 2015)

  • Runs TPC queries

  • Supports JOIN, sub-queries, derived tables

  • Secondary indexing strategies

    • Immutable for write-once/append only data (eg: Log data.. has time component). There is no incremental secondary index.
    • Global for read-heavy mutable data (co-processors on the server side keep indexes in sync with data tables). A second HBase table is created that is ordered according to the columns you are indexing. Optimiser will use this table if it deems this table to be more efficient.
    • Local for write-heavy mutable or immutable data. Custom load-balancers. All the writes are local. The writes can be done on the same region servers.
  • statistics driven parallel execution – background process in hbase. allows the user to optimze queries. uses equidistant keys in region to collect stats.

  • Tracing and metrics for monitoring and management – better visibility into what’s going on. Uses Apache HTrace. Collect data across scans into stats table. Then query for bottlenecks – index maint etc.,

Join and subquery support

  • Left/Right/Full outer Join; cross join

  • additional: semi join; anti-join

  • Algorithms: hash-joni; sort-merge-join

  • Optimizations:

    • Predicate push-down
    • FK-to-PK join optimisation
    • Global index with missing data columns
    • Correlated subquery rewrite
  • TPC - benchmarking suite

  • HBase 1.0 support

  • Functional indexes – 4.3 release of Ph. Indexing on expression not on column.

WHERe DAYOFMONTH(create_time)=1

Adding the following index will turn it into a range scan:

CREATE INDEX day_of_month_idx
  • User defined functions – extension points to phoenix. Define the metadata of the fun (input, output) params.
RETURN INTEGER AS 'org.apache.geo.woeidDistance'
USING JAR '/lib/geo/geoloc.jar'


SELECT FROM woeid a JOIN woeid b on a.country = b.country
WHERE woeid_distance(a.ID, b.ID) < 5;

This query executes on server side. This is tenant aware, which means.. there is no chance of conflict between tenants defining the same UDF.

  • Query Server + Thin Driver – offloads queryplanning and execution to different server(s). Minimises client dependencies

  • connection string: Connection conn = DriverManager.getConnection("jdbc:phoenix:thin:url=http://localhost:8765");

  • See http://phonenix.apache.org/server.html

  • Union ALL support – query multiple tables to get single resultset.

  • testing at scale with Pherf – define a scenario and deploy pherf to the cluster. It will generate the data and collect the stats. Testing with representattive data size. functional data sets.

  • MR Index build

  • Spark Integration .. RDD backed by a Phoe table.

  • Data built-in functions – WEEK, DAYOFMONTH etc.,

  • Tranactions – using http://tephra.io

    • supports repeable_read isolation level
    • allows reading your own uncommitted data
    • Optional – enable on a table by table basis
    • no performance penalty when not used
    • Optimistic concurrency control
    • no deadlocks or lock escalation
    • cost of conflict detection and possible rollback is higher
    • good id conflicts are rare: short transactions, disjoin partitioning of work
    • conflict detection is not necessary if the data is immutable. ie., write-once/append-only data.
  • Tephra Architecture

Apache Calcite

  • next step for Phoenix
  • Query parser, compiler and planner framework
  • SQL-92 compliant
  • Pluggable cost-based optimizer framework
  • Interop with Calcite adaptors
  • already used by Drill, Hive, Kylin, Samza
  • Supports any JDBC source
  • One Cost model to rule them all


Setting up a local phoenix cluster using Docker

Download and install Docker beta for Mac

We will setup a three node hadoop cluster using this recipe as a template.

git clone git@github.com:kiwenlau/hadoop-cluster-docker.git
cd hadoop-cluster-docker
docker build .

Lot of things fly by …

Successfully built ed4ece2b19f2

Create hadoop network

sudo docker network create --driver=bridge hadoop