Modern Cassandra for Developers
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The document discusses how Cassandra has been designed for massive scalability, high performance, reliability and availability without single points of failure. It highlights how Cassandra has been made easier to use through CQL3, native drivers, tracing, and other tools. Key features covered include CQL3 syntax, native drivers, request tracing, atomic batches, lightweight transactions, and triggers. The document promotes Cassandra's approachability and provides contact information for support and additional resources.
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Cassandra eu
This document discusses how the Dachis Group uses Cassandra and Hadoop for social business intelligence. They collect raw social media data and normalize it for analysis in Cassandra. Hadoop is used to calculate foundational metrics. The data is enriched and analyzed using Pig and Oozie workflows. Metrics are stored in Postgres. They launched products like the Social Business Index and Social Performance Monitor to measure social media effectiveness for companies. Lessons learned include dealing with big data bugs and involvement in open source communities.
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This document discusses using Pig with Cassandra to perform analytics and data processing tasks. Pig allows running queries over Cassandra data and storing intermediate results in HDFS or Cassandra. Example uses include analytics, data exploration, validation, and correction. Configuration involves splitting the cluster into virtual datacenters and setting properties. Future work includes improving data type handling and adding support for secondary indexes and wide rows.
Cassandra/Hadoop Integration
This document discusses integrating Cassandra with Hadoop to enable both online transaction processing (OLTP) and online analytical processing (OLAP) on the same data. It provides an overview of Cassandra and Hadoop, describes how to configure them together on the same or separate clusters, and highlights tools like Pig that enable analytics on Cassandra data using Hadoop and MapReduce. Real-world examples of companies using the integration are also listed.
Cassandra + Hadoop @ApacheCon
This document discusses using Apache Hadoop for analytics on data stored in Apache Cassandra. It describes how Cassandra is optimized for fast writes and tunable consistency, while Hadoop supports analytics through MapReduce and tools like Pig and Hive. The document provides a recipe for overlaying Hadoop on a Cassandra cluster to leverage data locality for analytics processing. Examples are given of using Hadoop streaming and Cassandra input/output formats to integrate the two systems.
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This document introduces Cassandra and Hadoop and how they can be used together for analytics over Cassandra data. It discusses how Cassandra is good for writes and random reads at scale but not ad-hoc queries, while Hadoop tools like MapReduce, Pig, and Hive can query Cassandra data and are extensible. It provides examples of using MapReduce and Pig with Cassandra and discusses how Raptr.com uses Cassandra and Hadoop together to improve query performance from hours to 10-15 minutes.
Cassandra+Hadoop
This document discusses using MapReduce with Cassandra. It describes how writing to Cassandra from MapReduce has always been possible, while reading was enabled starting with Cassandra 0.6.x. Using MapReduce with Cassandra provides analytics capabilities and avoids single points of failure compared to MapReduce with HBase. The document covers setup and configuration considerations like locality, and provides examples of a separate cluster approach and hybrid cluster approach. It also outlines future work like improving output to Cassandra and adding Hive support.
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Modern Cassandra for Developers
- 1. Modern Cassandra for Developers Jeremy Hanna Support Engineer ©2013 DataStax Confidential. Do not distribute without consent.
- 2. Solid Design •Massive scalability •Multi-datacenter •High Performance •Reliability/Availability •no SPOF, no special roles
- 3. Ease of Use?
- 4. Approachable •CQL3 •Cassandra native drivers •Native paging •Tracing •DevCenter •Atomic batches •Lightweight transactions •Triggers
- 5. CQL3 •Basic auth, similar to mysql • CREATE • GRANT USER bombadil WITH PASSWORD 'goldberry4ever' SUPERUSER; ALTER ON KEYSPACE shire TO gandalf; •Native drivers •Support for collections (list, map, set) •Hadoop support •Tracing •Native paging (2.0)
- 6. CQL3 examples CREATE KEYSPACE shire WITH REPLICATION = {'class': 'NetworkTopologyStrategy', 'eu' : 3, 'us-east' : 2}; SELECT * FROM emp WHERE empID IN (130,104) ORDER BY deptID DESC; INSERT INTO excelsior.clicks (userid, url, date, name) VALUES ( 3715e600-2eb0-11e2-81c1-0800200c9a66, ‘http://cassandra.apache.org', ‘2013-10-09', ‘Mary') USING TTL 86400; UPDATE users SET email = ‘charlie@wonka.com’ WHERE login = ‘cbucket64' IF email = ‘cbucket@wonka.com’
- 7. Native drivers •Asynchronous •allows multiple concurrent queries per connection •Server notifications •Made for CQL3 •Java, C#, Python, others in community
- 8. DevCenter
- 9. Request Tracing •Interactively using cqlsh •Programmatically using the Java driver •Probabilistic tracing
- 10. Various other goodies •Atomic batches for grouped updates •Lightweight transactions •Example: username creation •Heavy operation (4 round-trips) •Triggers (warning experimental in 2.0) •CREATE TRIGGER <name> ON <table> USING <classname>;
- 11. Questions? •Jeremy Hanna •Support engineer at DataStax •@jeromatron on twitter and #cassandra irc •See also •https://github.com/datastax/java-driver •http://www.datastax.com/what-we-offer/products-services/devcenter •http://www.datastax.com/docs •http://www.datastax.com/dev/blog