Partitioning data across multiple nodes is a fundamental strategy to handle the physical limitations of single-server databases. Effective partitioning ensures that data is evenly distributed, facilitates easy identification of data locations, and enables seamless scaling as the cluster grows. Here’s an in-depth look at the partitioning schemes and their implementations:
Key Requirements for Partitioning:
Even Distribution:
- Data should be evenly distributed across all cluster nodes to avoid hotspots and ensure balanced load.
Efficient Data Location:
- It should be possible to determine the node storing a particular data record without querying all nodes, thus reducing overhead.
Ease of Data Movement:
- Adding new nodes should involve minimal data movement, ensuring scalability without significant downtime or performance degradation.
Partitioning Schemes
Fixed Partitions
Use a hash of the key to determine the partition.
Example: partition = hash_of_key % no. of partitions
Logical Partitions
Logical partitioning involves dividing the data into many small, manageable pieces called logical partitions or shards. These logical partitions are then mapped to physical nodes in the cluster. The key idea is that the number of logical partitions is significantly higher than the number of physical nodes.
- Example: Akka suggests 10x logical partitions compared to nodes; Ignite uses a default of 1024 partitions.
- Adding nodes involves reassigning logical partitions, minimizing data movement.
Key Concepts
Logical Partitions vs. Physical Nodes:
- Logical Partitions: These are virtual divisions of the dataset. For example, if you have 1000 logical partitions and 10 physical nodes, each node might handle around 100 logical partitions.
- Physical Nodes: These are the actual servers or machines in the cluster.
Hashing and Mapping:
- Data records are assigned to logical partitions using a hashing function.
- Example:
partition = hash_of_key % no. of logical partitions - Each logical partition is then mapped to a physical node.
Reassignment and Scalability:
- When new nodes are added to the cluster, logical partitions can be reassigned to balance the load.
- Only the logical partitions that need to be moved are affected, minimizing data movement.
Key-Range Partitions
Use ranges of keys to determine partitions.
Example: 26 partitions for keys starting with each letter of the alphabet.
Benefits:
- Supports range queries efficiently.
- Example: Query for keys from ‘p’ to ‘q’ accesses only relevant partitions.
Challenges:
- Key ranges might not be known upfront.
- Mapping from key to partition can change over time as partitions split.
Dynamic Partitioning
Dynamic partitioning is a strategy used in distributed databases to manage data distribution and scalability more flexibly. Unlike fixed partitioning, where the number of partitions is predetermined, dynamic partitioning allows partitions to be created and adjusted as the data grows and changes. This approach helps to balance the load more effectively and supports efficient data access patterns, especially for range queries
- HBase: Implements dynamic partitioning, splitting partitions as they grow.
- YugabyteDB and CockroachDB: Support key-range partitions, adapting to data distribution needs over time.
Key Concepts of Dynamic Partitioning:
Initial Single Partition:
- The database starts with a single partition for simplicity.
- All data is initially stored in this partition.
Partition Splitting:
- As the data in the initial partition grows beyond a certain threshold, the partition is split into smaller partitions.
- Splitting is typically based on key ranges, where the key space is divided into smaller, more manageable ranges.
- For example, if the initial partition covered keys from ‘a’ to ‘z’, it might be split into two partitions: ‘a’ to ‘m’ and ’n’ to ‘z’. If the ‘a’ to ‘m’ partition continues to grow, it can be further split into ‘a’ to ‘g’ and ‘h’ to ‘m’. These new partitions can remain on Node A or be moved to other nodes if necessary.
Dynamic Adjustment:
- Partitions can be further split or merged based on data growth, access patterns, and load distribution.
- This dynamic adjustment ensures that no single partition becomes a bottleneck.
Minimal Data Movement:
- When a partition is split, the new partitions can initially stay on the same node to minimize data movement.
- Data is only moved if the partitions are reassigned to different nodes later.
Metadata Management:
- The system maintains metadata about the current partitioning scheme, including key ranges and node assignments.
- This metadata is used by client libraries to route requests to the correct nodes.
