Cassandra Partition Key FAQs
What is a Cassandra Cluster and How Does it Work?
Apart from making data unique, the partition key component of a primary key plays an additional significant role in the placement of the data. As a result, the Cassandra partition key improves reads and write performance of data spread across multiple nodes in a cluster.
The Cassandra partition key’s primary goal is to query data efficiently and evenly distribute data across a cluster. It is always the first value in the definition of the primary key.
A composite partition key is used to combine more than one column value to form a single partition key.
Without a Cassandra partition key in the where clause, a data fetch query results in an inefficient full cluster scan. Cassandra uses the consistent hashing technique in the where clause with a partition key to identify the exact partition range and node within the cluster. This makes the fetch data query more efficient and faster.
A Cassandra partition key range query is a scatter-gather query. To avoid impacting OTLP application request performance, limit this query to isolated data centers that only service analytics workloads.
Simple Primary Key
The Cassandra partition key is one column name for a table with a simple primary key. In these cases, the primary key consists only of the partition key. Under this arrangement, if many column values can distribute the partitions across many nodes, it is fast to insert and retrieve data stored with a simple primary key such as a basic id or text primary key.
Composite Partition Key
Cassandra allows you to use multiple columns as the partition key for a table with a composite partition key. Unlike a simple partition key, a composite partition key is used when the data stored is too large to reside in a single partition and determines where data will reside with multiple columns. Using a composite partition key breaks data into chunks with multiple columns and is helpful where hotspotting or writing data congestion is an issue. It also allows users to distribute results across multiple partitions for queries and return sorted data.
Compound Primary Key
Cassandra uses either a simple partition key or a composite partition key for a table with a compound primary key, and defines clustering column(s). A storage engine process, clustering sorts data based on the definition of the clustering columns within each partition. Typically, columns are placed in alphabetical ascending sort order. Generally, this simplistic choice is less beneficial for reads and writes than a different grouping of data.
When rows for a partition key are stored based on the clustering columns in order on a physical node, retrieval of rows is very efficient. Because only one table is accessed, the table is much more performant, even though the order of the data is the equivalent of JOINs in a relational database, based on clustering columns. Points are ordered in descending order for each category. Use a compound primary key for more complex querying needs.
Cassandra Partition Key vs Primary Key
A Cassandra primary key consists of one or more Cassandra partition keys, and possibly clustering key components. When the primary key consists of a single column, the Cassandra partition key is the same as the primary key and is responsible for distributing data among nodes. Cassandra is organized into a cluster of nodes, with partition keys belonging to nodes in about equal parts.
Cassandra Partition Key vs Clustering Key
The Cassandra partition key distributes data across nodes. The Cassandra clustering key sorts data within the partition.
Cassandra Partition Key Best Practices
Teams use the Cassandra data modeling process to analyze and define access patterns and data requirements on the required supportive data for a business process.
A successful data model aims to select a Cassandra partition key that evenly distributes data across cluster nodes in the cluster, bounds the size of the partition, and minimizes the number of partitions read by a single query.
Because Cassandra is a non-relational database, relational database best practices do not apply. For example, in Cassandra, writes are relatively cheaper, so denormalizing data can be a good practice. Learn more about SQL vs NoSQL.
The definition of a Cassandra schema is critical. Both the order of fields in the primary key and the way the sort order for each field is defined affect the final outcome. (Sort order defaults to ascending otherwise, as stated.)
Any team should first consider read and write patterns before designing the schema. This ensures they select the right partition and clustering keys to organize data for optimal read and write speed. Carefully consider partition size, data demographics, and data distribution when designing your schema as well.
Each Cassandra table has a standalone or composite partition key which determines data locality via indexing. Cassandra partition size is a crucial attribute for maintenance and performance, with ideal Cassandra partition size 10MB to 100MB. Several tools for analyzing, testing, and monitoring Cassandra partitions exist.
For a deep dive into Cassandra data modeling, see the following resources:
Wide Column Store NoSQL vs SQL Data Modeling video: NoSQL schemas are designed with very different goals in mind than SQL schemas. Where SQL normalizes data, NoSQL denormalizes. Where SQL joins ad-hoc, NoSQL pre-joins. And where SQL tries to push performance to the runtime, NoSQL bakes performance into the schema. Join us for an exploration of the core concepts of NoSQL schema design, using ScyllaDB as an example to demonstrate the tradeoffs and rationale.
Data Modeling and Application Development training course: This is an intermediate level course that explains basic and advanced data modeling techniques including information on workflow application, query analysis, denormalization and other NoSQL data modeling topics. After completing this course, you will be able to perform workflow application and query analysis, explain commonly used data types, understand collections and UDTs, and understand denormalization.
Data Modeling Best Practices: Migrating SQL Schemas for Wide Column NoSQL: To maximize the benefits of a wide column database like Cassandra, you must adapt the structure of your data. Data modeling for wide column databases should be query-driven based on your access patterns– a very different approach than normalization for SQL tables. In this video, you will learn how tools can help you migrate your existing SQL structures to accelerate your digital transformation and application modernization.
Does ScyllaDB Support Cassandra Partition Keys?
ScyllaDB is a modern high-performance NoSQL wide column store database that is API-compatible with Apache Cassandra. ScyllaDB supports Cassandra data models and parition keys. It also provides the benefit of deep architectural advancements that increase performance while reducing maintenance, overhead, and costs.
Cassandra was revolutionary when it first debuted in 2008, leading to its broad adoption. However, more than a decade later, many companies have recognized its underlying limitations and have now moved on. Leading companies such as Discord, Comcast, Fanatics, Expedia, Samsung, and Rakuten have replaced Cassandra with ScyllaDB. ScyllaDB delivers on the original vision of NoSQL — without the architectural downsides associated with Apache Cassandra (or the costs at volume of databases like Amazon DynamoDB). ScyllaDB is built with deep knowledge of the underlying Linux operating system and architectural advancements that enable consistently high performance at extreme scale.
Access white papers, benchmarks, and engineer perspectives on ScyllaDB vs Apache Cassandra.
