Introduction
Sharding is a pivotal concept in managing large-scale databases, especially when dealing with voluminous data that exceeds the storage capacity of a single server. This article explores the intricate world of sharding in ClickHouse, a robust open-source column-oriented database management system. We will delve into the basics of sharding, its benefits and drawbacks, and various methods to rebalance data in a sharded ClickHouse environment.
Understanding Sharding in ClickHouse
Sharding in ClickHouse involves horizontally splitting a large table (row-wise) across multiple servers. This is facilitated by ClickHouse’s distributed table engine, which efficiently processes sharded tables. Depending on the requirements, shards in ClickHouse can either be internally replicated for redundancy or remain non-replicated.
Benefits of Sharding
- High Availability: Sharding enhances data availability by distributing it across different servers.
- Faster Query Response: With data distributed, queries can be processed in parallel, significantly reducing response times.
- Increased Write Bandwidth: Sharding allows concurrent writes to multiple shards, increasing overall write capacity.
- Scalability: It is easier to scale out a database horizontally by adding more shards.
Challenges of Sharding
- Added Complexity: Sharding introduces complexity in database management and architecture.
- Data Imbalance: There’s a risk of uneven data distribution across shards, leading to potential performance bottlenecks.
Sharding Mechanics in ClickHouse
ClickHouse employs hash-based sharding. This involves choosing a column as the sharding key, hashing its values, and distributing the data based on the hash value. For example, in a setup with three shards, the row’s destination shard is determined by the remainder (modulo operation) of its hash value divided by the number of shards.
Distributed Table Engine
This engine plays a crucial role in ClickHouse’s sharding mechanism. It doesn’t store data independently but relies on other table engines (like the MergeTree family) for storage. It allows data insertion directly into the distributed table, where ClickHouse determines the appropriate shard based on the shard key, or manually into each shard’s underlying storage table.
Rebalancing Data in ClickHouse
Unlike some database systems, ClickHouse doesn’t support automatic shard rebalancing. Therefore, rebalancing requires manual intervention. Below are various methods for data rebalancing in ClickHouse, each with practical examples:
1. Adjust Sharding logic for Distributed Table
- Scenario: A distributed table with three 80% full shards gets a new fourth shard.
- Action: Adjust the sharding key, write policy, or adjust weight to favor the new shard, gradually balancing data distribution.
- Implementation: Preferentially write new data to the 4th shard. If 100 GB is written daily, configure 70 GB for the new shard and 10 GB for each existing shard.
2. Manually Writes to New Shard
- Scenario: Adding a new, empty shard to three fully utilized shards.
- Action: Temporarily redirect all new writes to the new shard until data distribution evens.
- Implementation: Update data ingestion scripts or configurations to target the new shard’s local table until its data volume matches the other shards.
3. Detach and Manually Relocate Partitions
- Scenario: Overloaded shards with month-partitioned data.
- Action: Detach a partition from an overloaded shard, move it to a new shard, and reattach.
- Implementation: Detach a specific month’s partition from an overloaded shard, transfer its data to the new shard, and reattach.
- More details: https://chistadata.com/parts-and-partitions-in-clickhouse-part-ii-manipulation-operations/
4. Create a New Cluster or Database and Use ClickHouse Copier
- Scenario: Severe imbalance in the current cluster.
- Action: Set up a new cluster with optimized sharding and migrate data using ClickHouse Copier.
- Implementation: Establish a new ClickHouse cluster with an optimized sharding scheme. Migrate data from the old cluster using ClickHouse Copier, possibly in stages.
- More details: https://chistadata.com/clickhouse-copier-a-reliable-workhorse-for-copying-data-across-clickhouse-servers/
5. Via Export/Import
- Scenario: Moving large datasets between shards.
- Action: Use SELECT OUTFILE/INFILE queries to transfer data in manageable chunks.
- Implementation: Execute select data into outfile queries to move data in intervals, like monthly data chunks.
- More details: https://chistadata.com/knowledge-base/clickhouse-backup-strategies-part-1/
6. Via Backup Utility
- Scenario: Moving large partitions between shards.
- Action: Take a backup of selected partitions that you want to migrate
- Implementation: use –partitions option in clickhouse-backup
- More details: https://chistadata.com/data-backup-and-restore-in-clickhouse/
Every method offers distinct advantages and disadvantages regarding resource utilization, manual effort, and its effect on production environments. Our upcoming blog post within this series will demonstrate practical examples of each possible solution to provide a deeper understanding of achieving resharding in ClickHouse with minimal impact or downtime to your production environment.
Note – Please note that there are ongoing internal discussions aimed at exploring new rebalancing strategies within the ClickHouse community. You are encouraged to engage in forums or internal meetings to discuss, develop, and stay updated on these topics, gaining insights into more efficient rebalancing techniques and data redistribution methods.
Conclusion
Sharding in ClickHouse offers a robust solution for managing large datasets, but it requires thoughtful consideration, especially regarding rebalancing data. The choice of a rebalancing method should be tailored to the specific needs of your database environment, considering factors like data volume, urgency, and resource availability. By understanding and applying these methods, database administrators and architects can ensure efficient, scalable, and reliable data storage and retrieval in ClickHouse environments.