This blog was originally published in July 2017 and was updated in August 2023.
It’s a pretty common question around here, so let’s see what we can do about that.
So, What is MySQL Partitioning?
Partitioning is a way in which a database (MySQL in this case) splits its actual data down into separate tables but still gets treated as a single table by the SQL layer.
When partitioning in MySQL, it’s a good idea to find a natural partition key. You want to ensure that table lookups go to the correct partition or group of partitions. This means that all SELECT, UPDATE, and DELETE should include that column in the WHERE clause. Otherwise, the storage engine does a scatter-gather and queries ALL partitions in a UNION that is not concurrent.
Generally, you must add the partition key into the primary key along with the auto-increment, i.e., PRIMARY KEY (part_id, id). If you don’t have well-designed and small columns for this composite primary key, it could enlarge all of your secondary indexes.
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What are the Different Types of MySQL Partitions?
Horizontal and vertical MySQL partitions are techniques used to divide tables into smaller sections for improved performance and management. Horizontal partitioning splits a table into smaller tables with identical columns but distinct rows, while Vertical partitioning divides a table into separate tables with related columns. Various partition types, like RANGE, LIST, HASH, and KEY, are used for specific needs, from range-based data to custom criteria, to ensure efficient data handling and the optimization of queries. Note: Vertical partitioning is not supported in MySQL 8.0 — more on this later.
RANGE partitioning
RANGE partitioning in MySQL is a data partitioning technique where a large table is divided into smaller partitions based on a specified range of column values like dates or numeric intervals. Each partition holds data that falls within a specific range, optimizing data handling and query speed.
HASH partitioning
HASH partitioning in MySQL divides a table into partitions based on the hash value of a designated column’s contents. Unlike range or list partitioning, where you manually designate the partition for specific column values, hash partitioning automatically assigns values to partitions based on hashing. This method distributes data evenly across partitions to achieve balanced storage and optimal query performance.
LIST partitioning
LIST partitioning in MySQL shares similarities with range partitioning. As with range partitioning, each partition is explicitly defined, but in list partitioning, partitions are created and assigned based on including a column value in predefined value lists rather than the contiguous ranges of values used in range partitioning.
COLUMNS partitioning
COLUMNS partitioning in MySQL is a technique that involves dividing a table into partitions based on specific columns’ values. Unlike other partitioning methods focusing on the entire row, column partitioning separates columns into different partitions. This approach is helpful when working with tables with many columns or when specific columns are frequently updated.
KEY partitioning
KEY partitioning is similar to HASH partitioning, except that only one or more columns to be evaluated are specified, and the MySQL server provides its own hashing function. These columns can contain other than integer values since the hashing function supplied by MySQL guarantees an integer result regardless of the column data type.
MySQL Partitioning in Version 5.7
MySQL version 5.7 introduced various features and capabilities for partitioning, enhancing its utility in managing large datasets. It enabled dividing large tables into smaller, manageable segments based on defined criteria. This facilitates improved data organization, query optimization, and maintenance.
In version 5.7, MySQL partitioning supports multiple partitioning types, including RANGE, LIST, HASH, KEY, and COLUMNS. Each type caters to different data distribution needs.
Using partitioning in a MySQL 5.7 environment offers several practical benefits. It significantly improves query performance by reducing the amount of data scanned during queries, which is especially helpful when dealing with large tables. Partition pruning, a feature in MySQL 5.7, ensures that only relevant partitions are accessed, further enhancing query efficiency. Additionally, partitioning aids in maintenance tasks like archiving and purging old data, as operations can be performed on individual partitions instead of the entire table.
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MySQL Partitioning in MySQL 8.0
MySQL 8.0 brought substantial advancements and enhancements to partitioning, significantly elevating its capabilities. This version introduces key features and optimizations that address limitations from previous iterations.
One major enhancement is the support for subpartitioning. MySQL 8.0 allows you to create subpartitions within existing partitions, providing an additional level of data segmentation. This feature facilitates even more precise data organization and management, allowing for complex use cases involving multi-level data hierarchies.
Additionally, MySQL 8.0 introduces automated list partitioning, simplifying partition creation through by enabling the database to determine the partition based on the values inserted automatically.
This version also notably integrates native backing for range and list partitioning of spatial indexes, amplifying geospatial query speed for substantial datasets. Enhancements to the query optimizer improve partition pruning for both single-level and subpartitioned tables, leading to improved query performance.
To sum it up, MySQL 8.0 significantly advances partitioning with features like subpartitioning, automatic list partitioning, and improved query optimization. These enhancements address limitations from previous versions, allowing for more complex data organization, streamlined management, and optimized query performance.
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What are the Benefits of MySQL Partitions?
MySQL partitioning offers several advantages in terms of query performance and maintenance:
Enhanced Query Performance: Partitioning improves query performance by minimizing the amount of data scanned during queries. As the data is distributed into smaller partitions, the database engine only needs to scan relevant partitions, leading to faster query responses.
Optimized Resource Utilization: Partitioning enables parallelism in query execution across partitions. This means that multiple partitions can be processed simultaneously, making better use of available hardware resources and further enhancing query performance.
Data Retention and Deletion: Partitioning simplifies the archiving or deleting of old data by targeting specific partitions, and enhancing data retention policies.
Reduced Overhead: Partitioning can significantly reduce the overhead of managing large tables. For example, when inserting or deleting data, the database engine only needs to modify the relevant partitions, which can be much faster than performing these operations on the entire table.
Streamlined Maintenance: Partitioning simplifies maintenance operations. For example, you can perform maintenance tasks like index rebuilds, statistics updates, or data archiving on specific partitions rather than the entire table, minimizing downtime and optimizing resource utilization.
Data Lifecycle Management: Partitioning supports efficient data lifecycle management. Old or infrequently accessed data can be stored in separate partitions or even archived, allowing for better control over data retention and optimization of storage resources.
Enhanced Scalability: Partitioning enhances the database’s ability to scale, as data can be distributed across different storage devices.
In summary, MySQL partitioning brings substantial advantages to both query performance and maintenance. It improves data retrieval speed, enhances resource utilization, streamlines maintenance operations, optimizes storage management, and reduces overheads associated with large tables. These benefits collectively contribute to a more efficient database environment.
What are the Challenges and Limitations of MySQL Partitions?
While there are lots of positives about using MySQL partitioning, there can also be challenges and limitations that users should be aware of:
Query Optimization Complexity: Although partitioning can enhance query performance, it requires queries to be designed with partitioning key considerations in mind. Inappropriately designed queries may not fully utilize partitioning benefits, leading to poor performance.
Limited Key Choices: Not all columns are suitable for partitioning keys. Choosing a proper partitioning key is crucial, and inappropriate selections can result in uneven data distribution across partitions, impacting performance.
Suboptimal Partitioning Strategies: Choosing the wrong partitioning strategy or key can lead to performance degradation. For instance, using partitioning on a table with a small number of rows may not provide significant benefits and can even worsen performance due to increased complexity.
Limited Parallelism: While partitioning allows for parallel processing, there might be limitations on how many partitions can be processed concurrently based on hardware resources, potentially impacting query performance.
Data Skewing: In some scenarios, data might not be uniformly distributed across partitions, causing “data skew.” This can lead to uneven resource utilization and slower performance for certain partitions.
Replication and Backup Issues: MySQL partitioning might impact the way data replication and backups are performed. Special considerations are needed to ensure these processes still work seamlessly after partitioning.
So, while MySQL partitioning does offer advantages, it also brings challenges and limitations related to complexity, maintenance, query optimization, and performance. Careful planning and continuous monitoring are crucial to facing these challenges and achieving optimal performance.
Performance Optimization with MySQL Partitioning
MySQL partitioning enhances query performance by enabling the database to focus on relevant data partitions during queries. This reduces the amount of data that needs to be scanned, resulting in faster data retrieval. For example, when querying a large table for specific date ranges, partitioning allows the engine to scan only relevant partitions containing data within those ranges.
Query execution plans are positively impacted by partitioning. The query optimizer recognizes partitioning schemes and generates execution plans that use partition pruning. This means the optimizer can skip unnecessary partitions, resulting in optimized query plans that use fewer resources and execute more quickly.
Partitioning influences indexing strategies by narrowing the scope of indexing. Instead of indexing the entire table, partitioning allows for more focused indexing. This minimizes index size and boosts efficiency, leading to faster query performance.
In scenarios where partitioning aligns with natural data distribution, such as time-series data or geographical regions, query execution time is significantly reduced. Queries that involve specific partitions can bypass irrelevant data; for instance, when searching for transactions within a certain date range, partitioning enables the database to search only the relevant partition.
Best Practices for Implementing MySQL Partitioning
With these best practices, you can ensure that your MySQL partitioning setup is efficient, well-maintained, and improves database performance.
Choose the Correct Partition Key: Select a partition key that aligns with your data distribution and query patterns. Common choices include time-based or range-based values.
Monitor Query Performance: Continuously monitor query performance after partitioning. Use tools like EXPLAIN to assess query execution plans.
Watch for Bloat: Over time, partitions can accumulate large amounts of data, leading to slow queries.
Proper Indexing: Partitioned tables benefit from proper indexing. Ensure that the chosen partition key is part of the primary or unique key. Additionally, consider indexing frequently queried columns to improve performance further.
Regular Maintenance: Perform routine maintenance tasks, such as purging old data from partitions, optimizing indexes, and rebuilding partitions.
Backup and Restore: As we mentioned earlier, partitioning can impact backup and restore strategies. Ensure your backup and restore procedures account for partitioned data to prevent data loss and ensure reliable recovery.
Test, Test, and Test Again: Before implementing partitioning in production, thoroughly test it in a controlled environment. This helps identify potential issues and fine-tune the partitioning strategy.
Documentation: Always be documenting! Be sure to include your partitioning strategy, why certain partition keys are used, and your maintenance procedures.
Talk to experts: If you’re new to partitioning or dealing with complex scenarios, consider consulting with experts.
Choosing the Right Partitioning Strategy
Selecting the appropriate partitioning strategy in MySQL involves carefully considering various factors, including:
Understanding your data’s nature and distribution. For range-based data, consider range partitioning, while list partitioning is suitable for discrete values. Hash partitioning evenly distributes data.
Analyzing query patterns to align the strategy with how data is accessed. Time-based queries benefit from range partitioning, while hash partitioning suits equally accessed values.
Matching the strategy to your database requirements. For archiving historical data, consider range-based on time. High-write workloads might favor hash or key partitioning for load balancing.
Watching for changes in data patterns. As data grows, a previously effective strategy might become less optimal. Periodically review and adjust.
Any partitioning strategy should improve query performance, not lead to suboptimal queries — test and benchmark strategies before implementation.
Ensuring the strategy aligns with maintenance operations. For example, rebuilding large partitions might often impact uptime. Select a strategy that minimizes disruptions.
Continuously monitoring query performance after implementation. Be ready to adjust your strategy as needed.
Evaluating how your chosen strategy accommodates future growth, as some strategies scale better with larger datasets.
Choosing the right partitioning strategy is pivotal to database performance. By aligning the strategy with your data’s characteristics and specific requirements, you ensure that your MySQL partitioning delivers the desired results.
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Proper MySQL partitioning optimizes databases by splitting large tables into smaller parts, enhancing query speed and data management while reducing overhead and making maintenance easier. But, users need to understand that careful planning, monitoring, and testing are vital to avoid any potential performance declines due to improper setup.
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Hi Manjot, I think PRIMARY KEY (part_id,id), where id is auto increment column, wont work. Because the auto increment column should be in first position while creating the composite primary key.
Mayank, the solution is simple: Also have INDEX(id). The only thing that AUTO_INCREMENT requires is that the id be first in some index. It does not require PK or UNIQUE. (However, without a uniqueness constraint it is possible to insert a duplicate id — but who would do that!)
I have used this technique several times. It provides the performance benefit of “clustering” on part_id, while still having a UNIQUE primary key. That is, it is beneficial for a SELECT … WHERE part_id = 123 with multiple rows of 123. And for SELECT … WHERE part_id BETWEEN … .
Note that PARTITIONing add no benefit to either of those SELECTs.
And, yet, you would be surprised at how few use cases get any performance benefit with PARTITIONing versus a better index on a non-partitioned table.
It all depends on your workload. I recommend partitioning in a lot of cases. There are many people out there with 2B row tables and only access the last 5% of it 99% of the time.
Can you provide a 5th use case where Partitioning improves performance? See http://mysql.rjweb.org/doc.php/partitionmaint for the 4 cases I know of.
(I can’t imagine how your example of Hash “load balancing” provides any performance benefit. Do you have ‘proof’?)
if I´m right, partitioning means that the table will be split in many files, giving the advantage to just read in one file with the small size instead of read in one big file, off course the queries should be designed for that.
exactly!
Indexes are also designed for cutting down on how much you need to read.
Thank you guys for the interesting discussion..
We are working on a project which if successful may have Billion of rows in a single table. Currently, we are working on to build a MVP and will do proof of value.
We are bit worried on the number of potential rows a single table may have. We were looking for guidance on partitioning vs index and/or both. We will have an variable (say customer ID in product table) which can be used for creating almost separate tables for each customer.
Would you suggest that we create a reference table which direct to a product table (which gets created) for a customer? Or we do partitioning only..