Breathtaking Scale: 75,000 Cassandra Nodes and 10 Petabytes of Data

Apple, a tech giant renowned for its secrecy, has kept its scaling strategies for iTunes, iMessage, and other wildly successful services tightly under wraps. While they did reveal their use of a massive Mesos cluster for Siri, information about their utilization of MongoDB, Hbase, Couchbase, and Cassandra has remained elusive, despite job postings hinting at their involvement.

Nonetheless, some valuable insights into Apple's Cassandra adoption have surfaced, providing valuable lessons for enterprises aspiring to replicate Apple's success.

A year ago, Apple said that it was running over 75,000 Cassandra nodes, storing more than 10 petabytes of data. At least one cluster was over 1,000 nodes, and Apple regularly gets millions of operations per second (reads/writes) with Cassandra.

It’s breathtaking if you stop to think about this scale.

There are eight features that make Cassandra powerful:

1. Big data ready: Partitioning over distributed architecture makes the database capable of handling data of any size: at a petabyte scale. Need more volume? Add more nodes.
2. Read-write performance: A single node is very performant, but a cluster with multiple nodes and data centers brings throughput to the next level. Decentralization (leaderless architecture) means that every node can deal with any request, read or write.
3. Linear scalability: There are no limitations on volume or velocity and no overhead on new nodes. Cassandra scales with your needs.
4. Highest availability: Theoretically, you can achieve 100% uptime thanks to replication, decentralization and topology-aware placement strategy.
5. Self-healing and automation: Operations for a huge cluster can be exhausting. Cassandra clusters alleviate a lot of headaches because they are smart — able to scale, change data replacement, and recover — all automatically.
6. Geographical distribution: Multi-data center deployments grant an exceptional capability for disaster tolerance while keeping your data close to your clients, wherever they are in the world.
7. Platform agnostic: Cassandra is not bound to any platform or service provider, which allows you to build hybrid-cloud and multi-cloud solutions with ease.
8. Vendor independent: Cassandra doesn’t belong to any of the commercial vendors but is offered by a non-profit open-source Apache Software Foundation, ensuring both open availability and continued development.

But scale isn’t the only thing Cassandra does well.

Decoding Cassandra's Data Structure and Distribution

Cassandra's exceptional architecture effortlessly handles and distributes vast amounts of data across numerous servers, ensuring minimal downtime. Each Cassandra node, along with the Cassandra driver, possesses knowledge of data allocation within a cluster (referred to as token awareness), enabling applications to access any server and receive rapid responses.

Distribution of Data Across Nodes:

Cassandra relies on key-based partitioning to organize its data effectively. Key components of Cassandra's data structure encompass:

1. Keyspace: Serving as a container for data, similar to a schema, it houses several tables.

2. Table: Comprising columns, a primary key, and rows, it stores data in partitions.

3. Partition: A group of rows sharing the same partition token, acting as a fundamental unit of access within Cassandra.

4. Row: A single, structured data item within a table.

Understanding Data Structure in Cassandra

Data in Cassandra is stored in partitions, representing sets of rows in a table distributed across the cluster. Each row contains a partition key, comprising one or more columns, which undergoes hashing to determine data distribution across cluster nodes.

Why Partitioning Matters

Partitioning plays a pivotal role in facilitating seamless scalability for Big Data. By breaking data into manageable chunks, it can be efficiently spread across numerous servers, effortlessly accommodating increasing volumes of data.

Working with Partition Keys

Once a partition key is set for a table, a partitioner transforms its value into tokens through hashing and assigns each node a token range. Cassandra then automatically distributes each row of data across the cluster based on the token value. Scaling up involves merely adding new nodes, leading to redistribution based on new token range assignments. Scaling down is equally straightforward.

Designing an Optimal Partition

Data architects must devise partitions that yield accurate and swift query results before creating a data model. Since changing a primary key for a table is not possible, creating a new table and migrating the new data becomes necessary. For guidance on crafting an effective partition, refer to our video tutorial, where we explore a real-life example in-depth.

All of this means, returning to Apple, that Cassandra offers Apple the scale and increasingly the analytical horsepower to tackle an ever-expanding array of applications.

More NoSQL at Apple

It’s telling, as Sandeep Parikh hints, that Apple ran into enough limitations with traditional relational databases, including the likelihood that they “cost way too much to scale out,” such that it actively uses Cassandra, MongoDB, and other NoSQL technologies.

Heck, Apple even went so far as to buy the company behind FoundationDB, a NoSQL database.

Central among those, at perhaps double the adoption (very roughly extrapolating from job listings), is Cassandra. Most companies don’t have Apple’s scale, but for those that aspire to them, Cassandra is worth a strong look.

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