This document provides an overview of Cassandra, an open source distributed database. It discusses Cassandra's query language (CQL), which is similar to SQL but only supports Cassandra operations. It also covers Cassandra's tabular data model with rows, columns, and strong schemas. The document reviews tools for working with Cassandra and best practices for data modeling and application methodology, emphasizing denormalization and idempotency over transactions. It notes limitations of batches, secondary indices, and lightweight transactions in Cassandra.

1. Cassandra: An Alien Technology That’s not so Alien

2. Who am I?
• Brian Hess
– Sr. Product Manger for Analytics
• Math and CS background
– Distributed Systems
– Algorithms
• Government data mining research
• Data warehousing (Netezza)
– SQL and data mining and UDF fun
• Joined Datastax 1.5 years ago
– New to NoSQL and Cassandra
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5. Agenda
The Good
• Query Language
• Tooling
• Conceptual Data
Model
The Different
• Application
Methodology
• Connections/Drivers
• Data Modeling
The Dangerous
• Batches
• Secondary Indices
• Lightweight
Transactions
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6. Cassandra Query Language
• Very much like SQL
– SELECT, INSERT, DELETE
– WHERE clauses
– CREATE TABLE, GRANT, etc, etc
• SQL syntax for Cassandra operations
– Only supports Cassandra operations
– Not trying to cover the full SQL space
• Missing several things:
– JOIN, GROUP BY, windowed aggregates, subqueries, WITH, etc
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7. Cassandra Query Language
• Pop Quiz: CQL or SQL:
1. SELECT a, b, c FROM myData;
2. SELECT a, b, c FROM myData ORDER BY a LIMIT 3;
3. SELECT cust_id, txn_id FROM txn WHERE cust_id=5;
4. SELECT sensor, MAX(temp) FROM txn WHERE sensor=3;
5. SELECT MAX(temp) FROM txn
6. SELECT a.id, a.info, b.moreinfo FROM a JOIN b ON
(a.id=b.id)
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8. Cassandra Query Language
• Pop Quiz: CQL or SQL:
1. SELECT a, b, c FROM myData;
2. SELECT a, b, c FROM myData ORDER BY a LIMIT 3;
3. SELECT cust_id, txn_id FROM txn WHERE cust_id=5;
4. SELECT sensor, MAX(temp) FROM txn WHERE sensor=3;
5. SELECT MAX(temp) FROM txn
6. SELECT a.id, a.info, b.moreinfo FROM a JOIN b ON
(a.id=b.id)
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9. Tabular Data Model
• Rows and Columns
– Like SQL, R data.frame, etc
• Strong schema
– Each column has a data type
– Custom data types including Map, Set, List, and UDTs
– Can mimic “old style thrift tables” with Map
• Most Cassandra tables really have a schema
– Most data really has a schema
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10. Tooling
• Cqlsh
– Command-line CQL interpreter
– Mainly used for management operations
• CREATE KEYSPACE, CREATE TABLE, etc
• GRANT, etc
– Singleton INSERTs
– Some light load/unload operations via COPY
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11. Tooling
• DevCenter
– DataStax tool
– “Toad for Cassandra”
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12. Data Modeling
• Start with the query
– How will you access this data
– Then create the schema optimized for those queries
– Store it multiple times if you must – “Query Tables”
• Uniqueness – what is an overwrite versus an insert?
– Partition keys – for fast lookups
– Clustering Columns – for ranges, etc
– Mix and match for different query patterns – Users by ID, Users by Email, etc
• No joins
– So, no star schema
– Denormalize!
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13. Application Methodology
• Instead of rolling back, we retry
• In SQL you try a transaction, and if error, then rollback
– Leverages transaction isolation and rollback
• In Cassandra you try, and if error, try again
– Leverages idempotent data model and high availability
• “Well, did you want to write that to the database or not?”
– “Instead of Transactions and Rollback, we have Idempotency and Retry”
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14. Drivers / Connections
• Multilanguage drivers
– Java, Python, C/C++, PHP, Ruby, etc
• Leverage the whole cluster
– Connect to all the nodes – connect to one and discovering the others
– Load balancing – Round Robin, etc
– Smart routing of queries – “Token Aware Routing”
• Not JDBC / ODBC
– Cassandra-specific, but similar
– Cluster, Session, Statement, ResultSet, etc
– Different data types, etc
– Lower-level configurations – number of connections, paging size, etc
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15. Batches
• These are not what you think they are
• How they work…
– Send all the statements to the coordinator
– If “logged”, then a batchlog is written for durability – and replicates it
– Executes each statement – involves other nodes (possibly)
• They are not done as an isolated set of INSERTs
– Well, they are if they all update the same partition (nuance)
– Things will be seen as they are executed
• They do not speed things up
– The coordinator has to do all the work – latency increase and timeouts
– You still need to talk to multiple nodes for multiple INSERTs
– Logged batches require a lot of work by the coordinator
• Maintain a batchlog for durability – and replicates it
• But, they do serve a purpose
– Inter-table consistency (logged batches)
– Some bulk load performance (unlogged batches)
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16. Secondary Indices
• These are not what you think they are
• Secondary Indices need to consult every Cassandra node
– Cassandra is optimized for “single node queries”
– Okay for single-partition queries, but not really worth it
• Maintaining secondary indices is also an overhead
• Basically, they rarely make things faster in cases that matter
• Consider Materialized Views in Cassandra 3.0
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17. Lightweight Transactions
• These are not what you think they are
• They are not SQL transactions
– They do not support roll-back, etc
• They do allow for serialization and isolation
– Useful for operations like creating accounts
– INSERT INTO users(username, email) VALUES
('cassandra_fan', 'abc@def.com') IF NOT EXISTS
• They are costly
– Paxos algorithm – multiple passes/rounds
• But they do serve a purpose
– Use sparingly
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18. Thank you

19. Cassandra?
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20. Cassandra!
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