Primary and Clustering Keys should be one of the very first things you learn about when modeling Cassandra data. Most people coming from a relational background automatically think, ""Yeah, I know what a Primary Key is"", and gloss right over it. Because of this, there always seems to be a lot of confusion around the topic of Primary Keys in Cassandra. This presentation will demystify that confusion. I will cover what the different types of Keys are, how they can be used, what their purpose is, and how they affect your queries. For this presentation, I will be using CrossFit gym locations as my subject matter. I will explain the differences between Primary Keys, Compound Keys, Clustering Keys, & Composite Keys. I will also show how the data behind each type differs as stored on disk. Lastly, I will show what queries each type of key will support. About the Speaker Adam Hutson Data Architect, DataScale Adam is Data Architect for DataScale, Inc. He is a seasoned data professional with experience designing & developing large-scale, high-volume database systems. Adam previously spent four years as Senior Data Engineer for Expedia building a distributed Hotel Search using Cassandra 1.1 in AWS. Having worked with Cassandra since version 0.8, he was early to recognize the value Cassandra adds to Enterprise data storage. Adam is also a DataStax Certified Cassandra Developer.

1. C* Keys: Partitioning, Clustering, & CrossFit
Adam Hutson - Data Architect, DataScale Inc.

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Who am I & What do we do?
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Adam Hutson
Data Architect @ DataScale -> www.datascale.io
DataStax MVP for Apache Cassandra
DataScale provides hosted data platforms as a service
Offering Cassandra & Spark, with more to come
Currently hosted in Amazon & Azure

3. Overview

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1 Why
2 Partition
3 Partition Key
4 Composite Partition Key
5 Clustering Columns
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Why give this presentation?
Partitioning & Clustering should be the foundation.
Too often glossed over.
Has the biggest impact to performance of the cluster
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6. Partition

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Partition Explained
• Token values can range from -263 to 263-1.
• Nodes in the cluster/ring are assigned a single
token.
• A node is responsible for the token value and
expands to the previous node’s token.
• A Partitioner decides where a partition key
maps onto the cluster/ring.
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Node #3 is responsible for tokens
from -1844674407370955162
to -5534023222112865485

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Partition Explained
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9. Partition Key

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Partition Key Explained
The Partition Key is:
• responsible for distribution of data amongst the nodes
• the first column defined in the PRIMARY KEY
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Partition Key Explained
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Partition Key Explained
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13. Composite Partition Key

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Composite Partition Key Explained
Using multiple columns for the token hash value.
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Composite Partition Key Explained
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Composite Partition Key Explained
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17. Clustering Columns

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Clustering Columns Explained
Clustering Columns are:
• responsible for sorting within the partition
• any column added to the Primary Key, past
the first column
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Clustering Columns Explained
Can be used for Hierarchical structured data.
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Clustering Columns Explained
Can be used for Time Series structured data.
CREATE TABLE member_log
( member text,
workout_date timestamp,
workout_duration text,
PRIMARY KEY (member, workout_date)
) WITH CLUSTERING ORDER BY (workout_date DESC);
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Clustering Columns Explained
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22. Thank You!
Questions?
Adam Hutson @AdamHutson
adam@datascale.io @DataScaleInc