SingleStore vs Apache Druid
Compare and contrast SingleStore and Apache Druid by architecture, ingestion, queries, performance, and scalability.
SingleStore vs Apache Druid Architecture
SingleStore is a proprietary distributed relational database that handles both transactional and analytical workloads. It relies on memory and a persistent cache to deliver low latency queries. For longer term data retention, SingleStore Cloud separates compute from cloud object storage. SingleStore Cloud pricing is based on compute and storage usage.
Druid’s architecture employs nodes called data servers that are used for both ingestion and queries. High ingestion or query load can cause CPU and memory contention compared with Druid alternatives. Breaking apart the pre-packaged ingestion and query server components involves planning ahead and additional complexity, and is not dynamic.
SingleStore vs Apache Druid Ingestion
SingleStore has integrations to common data lakes and streams. With SingleStore pipelines, users can perform common data transformations during the ingestion process. SingleStore provides limited support for semi-structured data with its JSON column type. Many users structure data prior to ingestion for optimal query performance.
Druid has built-in connectors that manage ingestion from common data sources. Unlike some Druid competitors, it doesn’t support nested data, so data must be flattened at ingest. Denormalization is also required at ingest, increasing operational burden for certain use cases.
SingleStore vs Apache Druid Performance
SingleStore has two storage formats: a rowstore and a columnar store referred to as universal storage. The columnar store is used for analytical workloads, loading data in batch and relying on memory to achieve seconds of data latency. The columnar store can also execute queries in seconds when the working set is contained in memory. SingleStore provides the ability to configure and manage additional indexes on the data for faster performance.
Druid is designed to make streaming data queryable as quickly as possible. JOINs are either impossible or incur a large performance penalty. Updates are only possible via batch jobs. Druid leverages data denormalization and write-time aggregation at ingestion to reduce query latency.
SingleStore vs Apache Druid Queries
SingleStore supports SQL as its native query language and can perform SQL joins. It is designed for querying structured data with static schemas. Users can create data APIs to execute SQL statements against the database over an HTTP connection. Common SingleStore use cases include business intelligence and analytics, and the database offers a number of integrations to visualization tools.
Druid has a native JSON-based query language and provides Druid SQL as an alternative that translates into its native queries. JOINs are not recommended.
SingleStore vs Apache Druid Scalability
SingleStore Cloud can be sized up or down using the UI or the Management API. There is no ability to scale out by increasing or decreasing the leaf and aggregator nodes in the cloud offering. In the self-managed offering, horizontal and vertical scaling can occur by updating command-line arguments or the cluster directly. Horizontal scaling does require rebalancing
Druid users are exposed to complex decisions about the number and size of servers as clusters are scaled.