Database sharding and partitioning are two techniques to improve the scalability and performance of large-scale applications that handle massive amounts of data. In this article, you will learn what they are, how they work, and how to implement them in your own projects.
Database sharding is the process of splitting a large database into smaller, independent, and distributed units called shards. Each shard contains a subset of the data, and can be located on a different server or cluster. Sharding reduces the load on each database server, and allows for parallel processing and querying of the data. However, sharding also introduces some challenges, such as data consistency, availability, and security.
A much simpler version of understanding database sharding would be, database sharding is like splitting a large book into smaller chapters and assigning each chapter to a different person to read. Similarly, in databases, sharding involves breaking up a large database into smaller, more manageable pieces called shards, and distributing those pieces across multiple servers. This helps improve performance and scalability by reducing the workload on any single server.
Sharding is an obsolete data replication model based upon (JVM & C compiler unprotected Memory threads lost in garbage collection off the OS memory Heaps) was limited by RDMS, eventual Consistency & Master Slave Election delays & speed of light limits. SSD Web enabled cache Replication Architecture w p2p SQLlite.org as the same Storage engine 4 OS, browser & App on mobile w/ 100 milisecond latency for sub 1 second World Scale User roundtrips, built on Browser & OS (Browser, App OS & Temp Memory Cache) for 3x Redundant Local Storage in SQLlite, Web Sockets & Protected memory iscolates Threads in Dart.org language that Google opensources in web workers Browser alternatives 2 sqllite in Html5 web browser standard.
Database partitioning is the process of dividing a large table or collection into smaller, more manageable units called partitions. Each partition contains a subset of the rows or documents, and can be stored on the same or different server. Partitioning improves the performance and efficiency of queries, by reducing the amount of data that needs to be scanned or indexed. However, partitioning also requires careful planning and design, as it affects the schema, indexing, and querying of the data.
A sharding key is an attribute or column that determines how the data is distributed among the shards. It is essential to choose a sharding key that balances the load and distributes the data evenly, while minimizing cross-shard queries and transactions. Common sharding keys include hash-based, range-based, and list-based. Hash-based keys assign data to a shard based on a hash function of the sharding key and create uniform distribution, but make it hard to perform range queries or joins. Range-based keys assign data to a shard based on a range of values of the sharding key and enable easy range queries and joins, but may lead to data skew or hotspots if the ranges are not well-defined or balanced. List-based keys assign data to a shard based on a predefined list of values of the sharding key and provide more control and flexibility over the data distribution; however, they require more maintenance and configuration.
Even Distribution: Choose a key that evenly distributes data across shards to avoid hotspots where one shard gets overloaded with data. Query Patterns: Consider how your data is queried. Choose a key that aligns with frequently used queries to minimize the need for cross-shard queries, which can be slower. Data Access Patterns: Understand how your application accesses data. Choose a key that reflects natural access patterns to minimize the need for data movement across shards. Join Operations: If your application frequently performs join operations, choose a key that minimizes the need for joins across shards. Business Logic: Consider any specific business requirements or constraints that may influence your choice of sharding key.
A partitioning strategy is the method or rule that determines how data is divided into partitions. It's important to choose a strategy that improves query performance and efficiency, while maintaining data integrity and consistency. Horizontal partitioning divides the data by row or document, based on a criteria or condition; this reduces the size of each partition and allows for faster scanning and filtering. Vertical partitioning divides the data by column or attribute, based on frequency or relevance; this reduces the amount of data that needs to be transferred or stored and allows for more selective querying. Hybrid partitioning combines both row and column-based criteria, allowing for more fine-grained and customized partitioning but also increasing overhead and complexity.
When implementing database sharding and partitioning, there are several factors to consider, such as the type and structure of your data, the volume and growth rate of your data, the frequency and pattern of your queries, the availability and reliability of your servers, and the scalability and performance of your application. Depending on your specific needs and goals, you may use different tools, such as Database Management Systems (DBMS) with native features like MongoDB, Cassandra or PostgreSQL, middleware or proxy layers like Vitess, Citus or ShardingSphere, or application-level custom sharding and partitioning logic with algorithms like consistent hashing, range partitioning or list partitioning.
Database sharding and partitioning can offer several advantages for your application, such as improved scalability and performance, enhanced availability and fault tolerance, as well as reduced cost and complexity. However, these techniques also come with some drawbacks, such as increased risk and difficulty, reduced functionality and compatibility, as well as higher overhead and trade-offs. Splitting and spreading the data across multiple servers or clusters can introduce more points of failure and complexity, while limiting the scope of queries and causing compatibility issues with existing tools. Additionally, adding more layers and logic to your database architecture can result in network latency, bandwidth consumption, synchronization delays, or data duplication.
Mucho cuidado con el particionado de bases de datos, esta técnica es el último recurso para aumentar el rendimiento. Estrategia de rendimiento 1) Acceso a datos mediante consultas - addModOne( ID, ... ) Elimina registro de cache y a?ade/modifica registro en DB. - getOne( ID ) Verifica si existe en caché, sino consulta DB y a?ade a caché. - find 2) Uso de índices - Campo ID de registro - Consultas cubiertas totalmente por un índice (operaciones "find") 3) Emplear servidor / cluster caché intermedio Se puede cachear los registros mediante su ID. 4) Sharding / replicación
- The biggest question to ask before sharding a database is whether you should do it at at all. It should be a last resort. It is a costly move that will permanently complicate your infrastructure and have long term impact on your team. - If you decide to shard, research a lot of options. Not all database sharding solutions are equal. They all have idiosyncrasies, pros, and cons. They do not all function the same way when it comes to how they distribute data, what options are available(like geo-located data), redundancy, or any other feature. You need to know exactly what you are getting for your specific use case.