One Pager Cheat Sheet

• The article discusses the importance of data partitioning, the process of dividing a large database or dataset into smaller, manageable partitions, highlighting its relevance in designing large-scale distributed systems, various strategies, real-world applications, management tools, and best practices.
• Data partitioning involves splitting a logical database across multiple physical databases on different server instances, improving scalability, performance, and availability by enabling distribution of data across multiple machines, intelligent load balancing, and isolation of failures, thus reducing contention and improving throughput.
• The article describes three common types of data partitioning: Horizontal Partitioning (Sharding), where different rows of data are stored in separate tables or databases; Vertical Partitioning, which splits different columns of a table into separate tables; and Directory-Based Partitioning, where a lookup service divides data across nodes based on a partition key.
• The article discusses different strategies for data partitioning, including Range-Based Partitioning where data is divided based on ranges such as date or timestamp, Hash-Based Partitioning that uses a hash function to determine data placement, List-Based Partitioning where each partition is assigned a list of values, Round-robin Partitioning for sequential data distribution, and Composite Partitioning that applies combinations of partitioning techniques.
• The statement is incorrect due to the assertion that hash-based partitioning uses a date range, but in reality it uses a hash function applied to a specific key to dictate data partitioning, rather than a range of values, a method instead used in range-based partitioning.
• Data partitioning has several challenges including dealing with hotspots and skew, rebalancing data when nodes are added/removed, maintaining data consistency and joins across shards, and ensuring fault tolerance for possible physical network partitions.
• Many data systems and services, such as databases (Cassandra, MongoDB, MySQL, SQL Server), Big Data Systems (Hadoop HDFS, HBase), Web Services (Google Spanner, Amazon DynamoDB), and Streaming Platforms (Kafka) use partitioning to scale, either by distributing database rows, using in-built partitioning schemes, distributing big data workloads, sharding data, or distributing message streams.
• There are numerous database tools and technologies available for managing partitioning, including sharding libraries and frameworks, databaseaware load balancers, rebalancing and auto-sharding tools, and monitoring dashboards.
• For effective data partitioning, plan partitioning schemes early, partition by the common query patterns, monitor and rebalance load on each partition, test failure scenarios and network partitions, and abstract partitions behind APIs for decoupling.
• Data partitioning enhances the construction of internet-scale distributed systems by boosting manageability, performance and availability, with effective management and combination of different partitioning schemes being critical for scalable system design.