Fast Write Database FAQs
How Does a Fast Read Write Database Work?
Fast-write databases optimize data writing quickly and efficiently using several strategies:
Write optimizations. Fast read-write database systems streamline the process of accepting and storing incoming data by minimizing disk input/output (I/O), reducing unnecessary indexing, and employing batch processing.
Memory caching. Many fast-write databases use in-memory data structures or caches to temporarily hold incoming data sets before persisting it to disk. This speeds the write process by reducing reliance on slower disk operations.
Log-structured approaches. Rather than updating existing records directly, log-structured storage (LSM tree) mechanisms first write incoming data to an append-only log on disk or in-memory database. This sequential writing reduces seek times and fragmentation, enabling faster writes.
Data partitioning and sharding. Sharding—partitioning data across multiple nodes or servers—affects both reads and writes and performance. Sharding allows databases to distribute workloads to handle a higher volume of writes. Each shard operates independently so no single database, server, or node can become overwhelmed, which can significantly enhance the overall write performance.
Sharding can also impact read performance, with varying effects based on the specific implementation and querying patterns. For example, while latency for read queries is likely to increase when data is partitioned across multiple shards, queries that target a specific shard or partition may benefit from improved read performance due to reduced data volume per shard.
Optimized indexing. Fast-write databases often use more streamlined indexing structures to speed the initial write process or defer complex indexing mechanisms for later stages of the process.
Asynchronous processing. Some high performance databases employ asynchronous processing, acknowledging write operations quickly but not reflecting them in all querying operations immediately. This approach speeds the write process, reducing latency.
Batch processing and compression. Compressing data before storage can achieve high throughput and improve write performance by reducing the amount of data written to disk. And aggregating small writes into larger batches before committing them to disk reduces overhead from individual write operations.
Fast Write Databases: Benefits and Uses Cases
The real-time analytics of fast write databases are suited to a range of applications that require immediate data analysis and insights and offer several advantages for various use cases:
Low latency applications. Fast write databases often provide low-latency responses to write operations, ensuring quick data storage and retrieval. This is critical for financial trading systems, online gaming, and other applications where real-time data processing is essential.
High throughput applications. Fast write databases excel in handling high volumes of write operations per second, making them ideal for applications that require rapid data ingestion, such as IoT devices, logging systems, and real-time analytics platforms.
Scalability. Designed to scale horizontally, fast read-write databases allow easy expansion across multiple nodes or servers and consistent performance even as data volume and the rate of write operations increase.
Data warehouses. These centralized repositories aggregate and store large volumes of data—structured, semi-structured, and unstructured—from various sources. Data warehouses provide a unified and historical view of data in support of business analysis, reporting, and decision-making processes that complements the fast-write database focus on real-time ingestion and processing of data, especially for rapid updates and analysis.
Time-series data storage. Fast write databases efficiently handle sequential data points with timestamps, enabling easy analysis and visualization and are commonly used to store time-series data, such as sensor data, server metrics, or application logs.
Event sourcing and command query responsibility segregation (CQRS). Because fast read-write databases separate reads and writes to optimize performance, they are well-suited to CQRS architectures. They also support event sourcing, which captures changes to an application’s state as a sequence of events.
Stream processing and message queues. Fast write databases can serve as backends for efficient high-speed stream or message queue processing frameworks.
High-frequency trading. Fast read-write databases can store and process trading data in real time in financial applications, facilitating rapid decision-making for traders.
Considerations and Tradeoffs of a Fast Write Database
If you opt for an LSM-tree based fast write-optimized database, be prepared for higher storage requirements and the potential for slower reads. When you work with immutable files, you’ll need sufficient storage to keep all the immutable files that build up until compaction runs.
You can mitigate the storage needs to some extent by choosing compaction strategies carefully. Plus, storage is relatively inexpensive these days.
The potential for read amplification is generally a more significant concern with write-optimized databases (given all the files to search through, more disk reads are required per read request).
But read performance doesn’t necessarily need to suffer. You can often minimize this tradeoff with a write-optimized database that implements its own caching subsystem (as opposed to those that rely on the operating system’s built-in cache), enabling fast reads to coexist alongside extremely fast writes. Bypassing the underlying OS with a performance-focused built-in cache should speed up your reads nicely, to the point where the latencies are nearly comparable to read-optimized databases.
With a write-heavy workload, it’s also essential to have extremely fast storage, such as NVMe drives, if your peak throughput is high. Having a database that can theoretically store values rapidly ultimately won’t help if the disk itself can’t keep pace.
Another consideration: beware that write-heavy workloads can result in surprisingly high costs as you scale. Writes cost around five times more than reads under some vendors’ pricing models. Before you invest too much effort in performance optimizations, and so on, it’s a good idea to price your solution at scale and make sure it’s a good long-term fit.
Read more in the Database Performance at Scale book– available for free.
Is Cassandra the Only Fast-Write NoSQL Database Option?
No. Cassandra is an open-source NoSQL database that can deliver fast write performance. However, there are many alternatives that also use LSM trees, including ScyllaDB.
Does ScyllaDB Offer Solutions for Fast Write Database?
Yes. ScyllaDB is optimized for write-heavy workloads since it stores data in immutable files (LSM trees). It supports fast writes because: 1) writes are sequential, which is faster in terms of disk I/O and 2) writes are performed immediately, without first worrying about reading or updating existing values (like databases that rely on B-trees do). As a result, you can typically write a lot of data with very low latencies.
Learn more about why ScyllaDB is an ideal database for fast writes and see our benchmarks for head-to-head comparisons with other databases.
