Understanding Data Partitioning for System Design Interviews
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Chapter 1: Introduction to Partitioning
When a system's data storage on a single node becomes excessive, it is typically divided into smaller segments, each stored on different nodes. The term "excessive" is subjective; however, it generally implies that the system can no longer accommodate additional data or that performance issues arise, such as slow querying or indexing, which do not meet service level agreements (SLAs).
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Each segment of data is commonly referred to as a partition, though different systems may have distinct terminology. For example:
- In Cassandra and Riak, a partition is known as a vnode.
- MongoDB, SolrCloud, and Elasticsearch refer to partitions as shards.
- HBase calls them regions.
- Bigtable uses the term tablet.
- Couchbase identifies partitions as vbuckets.
Section 1.1: Reasons and Benefits of Partitioning
The primary goal of data partitioning is to enhance scalability. Various partitioning methods exist, which we will explore further. Typically, partitioned data boosts query throughput within a datastore. The partitioning is executed in such a way that the smallest data unit, like a row, record, or document, belongs exclusively to one partition. This allows two queries accessing different partitions to run concurrently, and more complex queries involving multiple partitions can be executed more efficiently due to the ability to parallelize tasks and aggregate results later.
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For enhanced fault tolerance, higher query throughput, and better read availability, distributed systems often maintain replicated copies of partitions. A single node can manage multiple data partitions, serving as a leader for one while acting as a follower for another.
Section 1.2: An Analogy for Understanding Partitioning
To clarify the concept of partitioning, let's compare it to organizing a music collection. Imagine you have a vast array of CDs. One approach is to leave them in a random pile, searching through each disc until you find the one you want. Alternatively, you could sort them alphabetically and search through them, which is more efficient. However, the best method would be to create three distinct piles: one for artists whose names start with A-I, another for J-R, and a final one for S-Z, further sorting each pile. This method partitions your collection into three groups based on the first letter of the artist's name, allowing for a more efficient search, similar to how data partitioning optimizes queries in distributed systems.
Historically, Teradata and Tandem NonStop SQL (now part of Hewlett Packard Enterprise) were pioneers in implementing partitioned databases.
Chapter 2: Resources for System Design Interview Preparation
This video titled "Introduction to Partitioning | Systems Design Interview 0 to 1 with Ex-Google SWE" provides an in-depth look at partitioning in system design interviews, highlighting its significance and application.
The second video, "System Design Introduction For Interview," offers foundational knowledge on system design principles, valuable for interview preparations.