How Cassandra fits in traditional Architectures with RDBMS databases.

1. Does it mix? Cassandra and SQL Together!

2. About Pythian
• 18 Years of Data infrastructure
management consulting
• 200+ Top brands
• 6000+ databases under
management
• Over 400 DBA’s, in 35 countries
• Top 5% of DBA work force, 9
Oracle ACE’s, 2 Microsoft MVP’
s, 1 Cassandra MVP
• Oracle, Microsoft, MySQL,
Datastax partners, Netezza,
Hadoop and MongoDB plus
UNIX Sysadmin and Oracle
apps
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3. About Me
• Cassandra Consultant
• First contact was 0.8
• Cassandra MVP & Datastax
Certified Architect
• Lisbon Cassandra Meetup
• Passion for distributed systems
• Loves a good challenge
• Waterpolo is my sport
• @cjrolo
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4. 1 Cassandra Recap
2 Why?
3 How?
4 Q&A
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5. Cassandra Basics
• Cassandra is a highly scalable distributed masterless noSQL
database
• Column Store Architecture
• Log Structured Data
• CAP Theorem
• Eventually Consistent
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6. Cassandra vs RDBMS
• Scale up vs Scale out
• High availability vs Continuous availability
• Highly structured vs Semi-structured
• Replication is easy
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7. The Distributed Part
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8. Some (important) Details
• You need to know your reads before you write
• Replication Factor affects your availability, chose wisely!
• Per operation consistency
– All the way from "writes never fail" to "block for all replicas to be
readable".
• Update is a special case of Create, and Delete is not a real
Delete.
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9. The Good, the Bad and the Ugly
• The Good:
– Distributed
– No single point of failure
– It's easy to use
– You get to sleep at night!
• The Bad:
– SQL != CQL, can't just drop in
• The Ugly
– Not enough users!
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10. Why?
• Architectures evolve
• More data, faster processing
– Data is also moving
• Flexible, on-demand systems
• Scale...
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11. Architectures Then...
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12. Architectures Evolving...
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13. Architectures Now
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14. Yes! They do Mix!
• Velocity can serious hurt non-linear growing databases
• Fault tolerance is imperious
• Not all data have the same requirements!
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15. How?
• Some options:
– Build a side-system with Cassandra
– Put Cassandra in and have 2 data paths
• Other options available
– Replicate from RDBMS to Cassandra
– Hadoop/Spark/etc... data from one to the other
– Etc...
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16. Not so fast...
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17. Opportunities
• New applications
– loose data model avoids app re-writing, data migration, etc...
• Augmentation
• Scale out better.
• Have a data model that allows Cassandra absorb high velocity
data
• Absorb traffic from several locations
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18. Pitfalls (1)
• Hardware is important
– Get SSDs...
• Cassandra "just works"
– Can lead to overlooking how the system is performing
• Cassandra is new and is changing fast,
– Follow DSE Entreprise releases!
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19. Which Cassandra to pick?
• Rule of thumb:
– Follow DSE releases
• Use the community (mailing list, IRC, Etc...)
• DSE is an option for a all-in-one package!
– Search
– Analytics
– Security
– Etc...
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20. Q&A
• Thanks for listening!
• Questions?
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21. Thank you