Data partitioning and sharding

Data partitioning and sharding introduction

Data partitioning is a technique used in distributed systems and databases to divide a large dataset into smaller, more manageable parts, referred to as partitions. Each partition is independent and contains a subset of the overall data.

In data partitioning, the dataset is typically partitioned based on a certain criterion, such as data range, data size, or data type. Each partition is then assigned to a separate processing node, which can perform operations on its assigned data subset independently of the others.

Data partitioning can help improve the performance and scalability of large-scale data processing applications, as it allows processing to be distributed across multiple nodes, minimizing data transfer and reducing processing time. Secondly, by distributing the data across multiple nodes or servers, the workload can be balanced, and the system can handle more requests and process data more efficiently.

Data partitioning and sharding terminology

• Partition

  • A partition is a smaller, more manageable part of a larger dataset, created as a result of data partitioning.

• Partition key

  • The partition key is a data attribute used to determine how data is distributed across partitions. An effective partition key should provide an even distribution of data and support efficient query patterns.

• Shard

  • A shard is a term often used interchangeably with a partition, particularly in the context of horizontal partitioning (will be discussed later).

Partitioning methods

Designing an effective partitioning scheme can be challenging and requires careful consideration of the application requirements and the characteristics of the data being processed. Below are three of the most popular schemes used by various large-scale applications.

Horizontal partitioning

Also known as sharding, horizontal data partitioning involves dividing a database table into multiple partitions or shards, with each partition containing a subset of rows. Each shard is typically assigned to a different database server, which allows for parallel processing and faster query execution times.

For example, consider a social media platform that stores user data in a database table. The platform might partition the user table horizontally based on the geographic location of the users, so that users in the United States are stored in one shard, users in Europe are stored in another shard, and so on. This way, when a user logs in and their data needs to be accessed, the query can be directed to the appropriate shard, minimizing the amount of data that needs to be scanned.

The key problem with this approach is that if the value whose range is used for partitioning isn’t chosen carefully, then the partitioning scheme will lead to unbalanced servers. For instance, partitioning users based on their geographic location assumes an even distribution of users across different regions, which may not be valid due to the presence of densely or sparsely populated areas.

Vertical partitioning

Vertical data partitioning involves splitting a database table into multiple partitions or shards, with each partition containing a subset of columns. This technique can help optimize performance by reducing the amount of data that needs to be scanned, especially when certain columns are accessed more frequently than others.

For example, consider an e-commerce website that stores customer data in a database table. The website might partition the customer table vertically based on the type of data, so that personal information such as name and address are stored in one shard, while order history and payment information are stored in another shard. This way, when a customer logs in and their order history needs to be accessed, the query can be directed to the appropriate shard, minimizing the amount of data that needs to be scanned.

Hybrid partitioning

Hybrid data partitioning combines both horizontal and vertical partitioning techniques to partition data into multiple shards. This technique can help optimize performance by distributing the data evenly across multiple servers, while also minimizing the amount of data that needs to be scanned.

For example, consider a large e-commerce website that stores customer data in a database table. The website might partition the customer table horizontally based on the geographic location of the customers, and then partition each shard vertically based on the type of data. This way, when a customer logs in and their data needs to be accessed, the query can be directed to the appropriate shard, minimizing the amount of data that needs to be scanned. Additionally, each shard can be stored on a different database server, allowing for parallel processing and faster query execution times.

Data sharding techniques

Data sharding, a type of horizontal partitioning, is a technique used to distribute large datasets across multiple storage resources, often referred to as shards. By dividing data into smaller, more manageable pieces, sharding can improve performance, scalability, and resource utilization. Below are several data sharding techniques with examples.

Range based sharding

In range-based sharding, data is divided into shards based on a specific range of values for a given partitioning key. Each shard is responsible for a specific range, ensuring that the data is distributed in a predictable manner.

Example: An e-commerce platform stores order data and decides to shard it based on order dates. Shards can be created for specific date ranges, such as monthly or yearly intervals. When a query targets a specific date range, only the relevant shard needs to be accessed, which improves query performance.

Hash based sharding

Hash-based sharding involves applying a consistent hash function to the partitioning key, which generates a hash value that determines the destination shard for each data entry. This method ensures an even distribution of data across shards and is particularly useful when the partitioning key has a large number of distinct values or is not easily divided into ranges.

Example: A social media platform stores user data and decides to shard it based on user IDs. The platform applies a hash function to the user ID, generating a hash value that determines the shard for each user’s data. This approach ensures an even distribution of data across shards, optimizing storage efficiency and query performance.

Directory based sharding

Directory-based sharding uses a lookup table, often referred to as a directory, to map each data entry to a specific shard. This method offers greater flexibility, as shards can be added, removed, or reorganized without the need to rehash or reorganize the entire dataset. However, it introduces an additional layer of complexity, as the directory must be maintained and kept consistent.

Example: An online gaming platform stores player data and decides to use directory-based sharding. The platform maintains a directory that maps each player’s username to a specific shard. When a query targets a specific player’s data, the system first consults the directory to determine the relevant shard, then retrieves the data from that shard.

Geographical sharding

Geographical sharding involves partitioning data based on geographical locations, such as countries or regions. This method can help reduce latency and improve performance for users in specific locations by storing their data closer to them.

Example: A global streaming service stores user data and decides to shard it based on the user’s country. Each shard contains data for users from a specific country, and these shards are stored in data centers located within or near that country. This approach ensures that users can access their data with lower latency, improving the streaming experience.

Dynamic sharding

Dynamic sharding is an adaptive approach that automatically adjusts the number of shards based on the data’s size and access patterns. This method can help optimize resource utilization and performance by creating shards as needed and merging or splitting them as the data grows or shrinks.

Example: An Internet of Things (IoT) platform collects sensor data from a large number of devices. The platform uses dynamic sharding to automatically adjust the number of shards based on the volume and frequency of incoming data. As more devices are added or removed, the platform can create or merge shards accordingly, ensuring optimal resource utilization and performance.

Hybrid sharding

Why stick to one method when you can combine a few and get the best results? Hybrid Sharding is a blend of multiple sharding strategies to optimize performance. It might combine Geo-based with Directory-based sharding, or any other mix that suits a system's needs.

• Its Strength

  • By tailoring solutions and leveraging the strengths of different techniques, systems can achieve unprecedented efficiency levels.

• Snapshot

  • Many cloud service providers, given their diverse clientele and global infrastructure, adopt hybrid sharding. It's their secret sauce to ensure consistent, high-speed services across the board.

Data partitioning criteria

Data partitioning criteria are the factors or characteristics of data that can be used to divide a large dataset into smaller parts or partitions. Here are some of the most common criteria used for data partitioning:

Key or Hash-based Partitioning

Under this scheme, we apply a hash function to some key attributes of the entity we are storing; that yields the partition number. For example, if we have 100 DB servers and our ID is a numeric value that gets incremented by one each time a new record is inserted. In this example, the hash function could be 'ID % 100', which will give us the server number where we can store/read that record. This approach should ensure a uniform allocation of data among servers. The fundamental problem with this approach is that it effectively fixes the total number of DB servers, since adding new servers means changing the hash function which would require redistribution of data and downtime for the service. A workaround for this problem is to use 'Consistent Hashing'.

List partitioning

In this scheme, each partition is assigned a list of values, so whenever we want to insert a new record, we will see which partition contains our key and then store it there. For example, we can decide all users living in Iceland, Norway, Sweden, Finland, or Denmark will be stored in a partition for the Nordic countries.

Round-robin partitioning

This is a very simple strategy that ensures uniform data distribution. With 'n' partitions, the 'i' tuple is assigned to partition (i mod n).

Composite Partitioning

Under this scheme, we combine any of the above partitioning schemes to devise a new scheme. For example, first applying a list partitioning scheme and then a hash-based partitioning. Consistent hashing could be considered a composite of hash and list partitioning where the hash reduces the key-space to a size that can be listed.

Data partitioning advantages

Improved query performance

Data partitioning can significantly improve query performance by reducing the amount of data that needs to be processed for a given query. When data is partitioned, queries can be targeted at specific partitions, enabling the system to retrieve only the necessary data and ignore irrelevant partitions.

Example: An online bookstore uses range partitioning to divide its inventory data based on book genres. When a customer searches for mystery novels, the system only needs to query the mystery partition, avoiding the need to search through data for other genres, such as romance or science fiction.

Enhanced scalability

Partitioning data across multiple storage resources allows for greater system scalability. As the dataset grows, new partitions can be added to accommodate the additional data, without negatively impacting the performance of existing partitions.

Example: A social media platform uses horizontal partitioning to divide user data based on the user’s registration date. As more users join the platform, new partitions are created to store the data for these new users, ensuring that the system remains scalable and responsive as it grows.

Load balancing

Data partitioning helps distribute the workload evenly across multiple storage nodes or servers. This load balancing ensures that no single node becomes a bottleneck, leading to better overall system performance and reliability.

Example: A messaging service uses round-robin partitioning to distribute messages across multiple storage nodes. Each message is assigned to a different node in a cyclic manner, ensuring that the workload is balanced and no single node is overwhelmed with too many messages.

Data isolation

Partitioning data can provide a level of data isolation, where the failure or corruption of one partition does not necessarily impact the other partitions. This isolation can help improve the overall robustness and resilience of the system.

Example: A financial institution uses vertical partitioning to separate sensitive customer information, such as social security numbers and account numbers, from less sensitive data, such as transaction history. In the event of a data breach, the impact can be limited to only the affected partition, protecting the remaining data from potential exposure.

Parallel processing

Data partitioning enables parallel processing, where multiple partitions can be processed simultaneously by different processors or systems. This parallelism can lead to significant performance improvements, especially for large-scale data processing tasks.

Example: An e-commerce company uses horizontal partitioning to divide customer order data based on geographical regions. During peak sales periods, each regional partition can be processed by a separate server, allowing for faster processing of orders and reduced system bottlenecks.

Storage efficiency

By partitioning data based on usage patterns or data relevance, organizations can achieve more efficient storage utilization. Frequently accessed data can be stored on faster, more expensive storage resources, while less critical data can be stored on cheaper, slower storage resources.

Example: A video streaming service uses vertical partitioning to store high-resolution video files separately from lower-resolution versions. By storing high-resolution files on high-performance storage resources and lower-resolution files on more cost-effective storage resources, the service can optimize storage efficiency while maintaining high-quality streaming for users.

Simplified data management

Data partitioning can make data management tasks, such as backup, archiving, and maintenance, more manageable and efficient. By dealing with smaller, more focused partitions, these tasks can be performed more quickly and with less impact on overall system performance.

Example: An online news platform uses range partitioning to store articles based on their publication dates. This approach allows the platform to easily archive older articles or perform backups on specific date ranges without affecting the performance of the entire dataset.

Better resource utilization

Partitioning data based on specific attributes or access patterns can lead to better resource utilization. By aligning the data with the appropriate storage and processing resources, organizations can maximize the performance and efficiency of their data-driven systems.

Example: A weather forecasting service uses horizontal partitioning to store weather data based on geographical locations. This allows the service to allocate more resources to process data for areas with higher user demand, ensuring that resources are used efficiently and in line with user needs.

Improved data security

Data partitioning can help enhance data security by segregating sensitive information from less sensitive data. By isolating sensitive data in separate partitions, organizations can implement stronger security measures for those partitions, minimizing the risk of unauthorized access or data breaches.

Example: A healthcare provider uses vertical partitioning to separate patient medical records from demographic data. By storing sensitive medical records in a separate partition with strict access controls and encryption, the provider can better protect patient privacy and comply with data protection regulations.

Faster data recovery

In the event of a system failure or data loss, partitioning can help speed up the data recovery process. By focusing on recovering specific partitions rather than the entire dataset, organizations can reduce downtime and restore critical data more quickly.

Example: A multinational corporation uses horizontal partitioning to store sales data based on regional markets. If a system failure occurs, the company can prioritize the recovery of the most critical regional partitions, ensuring that essential operations can be resumed as soon as possible.

Data partitioning disadvantages

On a partitioned database, there are certain extra constraints on the different operations that can be performed. Most of these constraints are due to the fact that operations across multiple tables or multiple rows in the same table will no longer run on the same server. Below are some of the constraints and additional complexities introduced by Partitioning:

Joins and denormalization

Performing joins on a database that is running on one server is straightforward, but once a database is partitioned and spread across multiple machines it is often not feasible to perform joins that span database partitions. Such joins will not be performance efficient since data has to be compiled from multiple servers. A common workaround for this problem is to denormalize the database so that queries that previously required joins can be performed from a single table. Of course, the service now has to deal with denormalization's perils, such as data inconsistency.

Referential integrity

As we saw that performing a cross-partition query on a partitioned database is not feasible; similarly, trying to enforce data integrity constraints such as foreign keys in a partitioned database can be extremely difficult.

Most RDBMS do not support foreign keys constraints across databases on different database servers. This means, applications that require referential integrity on partitioned databases often have to enforce it in application code. Often in such cases, applications have to run regular SQL jobs to clean up dangling references.

Rebalancing

There could be many reasons we have to change our partitioning scheme:

• The data distribution is not uniform, e.g., there are a lot of places for a particular ZIP code that cannot fit into one database partition.
• There is a lot of load on a partition, e.g., there are too many requests being handled by the DB partition dedicated to user photos.

In such cases, either we have to create more DB partitions or have to rebalance existing partitions, which means the partitioning scheme changed and all existing data moved to new locations. Doing this without incurring downtime is extremely difficult. Using a scheme like directory-based Partitioning does make rebalancing a more palatable experience at the cost of increasing the complexity of the system and creating a new single point of failure (i.e. the lookup service/database).

Complexity

Data partitioning adds complexity to system architecture, design, and management. Organizations must carefully plan and implement partitioning strategies, taking into account the unique requirements of their data and systems. This added complexity can lead to increased development and maintenance efforts, as well as a steeper learning curve for team members.

Data skew

In some cases, data partitioning can result in uneven data distribution across partitions, known as data skew. This can happen when the chosen partitioning key or method does not distribute data evenly, leading to some partitions being larger or more heavily accessed than others. Data skew can result in reduced performance and resource utilization, negating the benefits of partitioning. For instance, if you were to shard a global customer database based on countries, and a vast majority of your users are from the US, then the shard containing US data might get overwhelmed.

Partitioning key selection

Choosing the appropriate partitioning key is crucial for achieving the desired benefits of data partitioning. An unsuitable partitioning key can lead to inefficient data distribution, performance bottlenecks, and increased management complexity. Selecting the right key requires a deep understanding of the data and its access patterns, which can be challenging for some organizations.

Cross-partition queries

When queries need to access data across multiple partitions, performance can suffer, as the system must search through and aggregate data from several partitions. This can result in increased query latency and reduced overall performance, especially when compared to a non-partitioned system.

Data migration

Partitioning can sometimes require significant data migration efforts, especially when changing partitioning schemes or adding new partitions. This can be time-consuming and resource-intensive, potentially causing disruptions to normal system operation.

Partition maintenance

Managing and maintaining partitions can be a challenging and resource-intensive task. As the data grows and evolves, organizations may need to reevaluate their partitioning strategies, which can involve repartitioning, merging, or splitting partitions. This can result in additional maintenance overhead and increased complexity. Here are a few other maintenance challenges:

• Backup Challenges

  • Performing backups isn't straightforward anymore. You need to ensure data consistency across all shards.

• Patch Management

  • When a security update rolls out, it needs to be applied across all shards, sometimes simultaneously, to maintain compatibility and security.

• Monitoring Woes

  • Instead of one set of metrics, DB administrators now need to monitor multiple, making anomaly detection a more daunting task.

Cost

Implementing a data partitioning strategy may require additional hardware, software, or infrastructure, leading to increased costs. Furthermore, the added complexity of managing a partitioned system may result in higher operational expenses.