A streaming database is a type of database that is designed specifically to process large amounts of real-time streaming data. Unlike traditional databases, which store data in batches before processing, a streaming database processes data as soon as it is generated, allowing for real-time insights and analysis. Unlike traditional stream processing engines that do not persist data, a streaming database can store data and respond to user data access requests. Streaming databases are ideal for latency-critical applications such as real-time analytics, fraud detection, network monitoring, and the Internet of Things (IoT) and can simplify the technology stack.
In today's fast-paced digital world, harnessing real-time data is crucial for organizations seeking to make informed decisions and stay competitive. Streaming databases with their unique features and advantages, have become the go-to choice for business teams to recommend to their IT partners.
This article will highlight three top open-source streaming databases that excel in real-time analytics.
ksqlDB
ksqlDB, developed and maintained by Confluent, is a stream processing SQL engine designed specifically for Apache Kafka. It's built on Kafka Streams, a client library used for creating stream processing applications on Kafka topics. ksqlDB operates under the Confluent Community License Agreement.
ksqlDB is tightly integrated with Kafka. To deploy ksqlDB, a Java runtime environment and access to a running Kafka cluster are required. If you plan to use Kafka Connect for ingesting and delivering event streams, a Kafka Connect cluster is also necessary. ksqlDB can be scaled both vertically and horizontally.
Data is ingested from and sunk to Apache Kafka by ksqlDB. It can also integrate with external systems using Kafka Connect. ksqlDB supports common serialization formats like JSON, Avro, and Protobuf.
ksqlDB offers:
- Streams and Tables: A stream is an immutable, append-only sequence of events representing the history of changes. A table contains the current status of events, which is the result of multiple changes. ksqlDB creates relations with Kafka topic data by applying a schema.
- Materialized view: Materializing is the process of converting the stream of events into a table with all the required changes. A materialized view is also known as stateful aggregation.
- Push Queries: A push query is a continuous query issued by a client that subscribes to a result, which changes in real-time. Push queries allow you to query a stream or materialized table with a subscription to the results.
- Pull Queries: A pull query is a query issued by a client that retrieves a result as of "now", similar to a query against a traditional RDBMS. Pull queries allow you to fetch the current state of a materialized view, which are incrementally updated as new events arrive.
- Connect: Kafka Connect is an open-source component of Apache Kafka. It handles the loading and exporting of data from an external system to Kafka. ksqlDB provides the functionality to create, describe, and import topics from Connect.
RisingWave
RisingWave is a distributed SQL streaming database open-sourced under the Apache 2.0 license.
RisingWave supports a range of data sources and sinks, including messaging systems, OLAP databases, data warehouses, data lakes, and OLTP databases. It also offers advanced stream processing features like exactly-once semantics, watermarks, and window functions.
Recently, RisingWave introduced the Forever-Free Developer Tier, enabling developers to innovate and build real-time applications without any financial constraints. For developers looking to set up a local single-node instance, RisingWave introduced the Standalone deployment mode, which eliminates the need to configure RisingWave, allowing users to get started with just a single command.
- Simple to learn: RisingWave speaks PostgreSQL-style SQL, enabling users to dive into stream processing in much the same way as operating a PostgreSQL database.
- Simple to develop: RisingWave operates as a relational database, allowing users to decompose stream processing logic into smaller, manageable, stacked materialized views, rather than dealing with extensive computational programs.
- Simple to integrate: With integrations to a diverse range of cloud systems and the PostgreSQL ecosystem, RisingWave boasts a rich and expansive ecosystem, making it straightforward to incorporate into existing infrastructures.
- Highly efficient in complex queries: RisingWave persists internal states in remote storage such as S3, and users can confidently and efficiently perform complex streaming queries (for example, joining dozens of data streams) in a production environment, without worrying about state size.
- Transparent dynamic scaling: RisingWave's state management mechanism enables near-instantaneous dynamic scaling without any service interruptions.
- Instant failure recovery: RisingWave's state management mechanism also allows it to recover from failure in seconds, not minutes or hours.
HStreamDB
HStreamDB is a streaming database provided under the BSD license. It is crafted to facilitate real-time data integration and synchronization. The platform adopts a cloud-native architecture, separating computation and storage layers for scalable and independent horizontal growth. It borrows ideas from frameworks like Kafka Connect, Pulsar IO, and Airbyte to develop HStream IO, which aids in data integration with external systems.
HStreamDB's fine-tuned storage engine guarantees low-latency persistent storage for streaming data and duplicates data across multiple storage nodes for improved reliability. It supports hierarchical data storage and automated migration of historical data to cost-effective storage services. The platform uses a publish-subscribe model for swift data subscription delivery, even during cluster failures.
Concentrating on flexibility, scalability, and efficient growth, HStreamDB provides online cluster scaling. This allows for dynamic expansion and contraction without requiring data repartitioning or significant data copying. In summary, HStreamDB strives to supply a comprehensive solution for managing real-time streaming data through its adaptable architecture and integration capabilities.
HStreamDB offers:
- Reliable, low-latency streaming data storage: Provides low latency and reliable storage through optimized design and data replication, with scalable storage capacity.
- Easy management of large scale data streams: Efficiently manages large data streams with stable performance and supports millions of streams in a single cluster.
- Real-time, orderly data subscription delivery: Uses publish-subscribe model for low-latency data delivery and ensures ordered delivery during cluster failures.
- Built-in powerful stream processing support: Offers event-time-based solution with features like basic filtering, key aggregation, and stream joining.
- Real-time analysis based on materialized views: Provides a materialized view for complex queries and real-time data insights through SQL query.
- Easy integration with multiple external systems: Acts as an enterprise data hub, managing all data access and flow, and connecting various services and systems.
- Cloud-native architecture, unlimited horizontal scaling: Allows independent scaling for compute and storage layers, supporting efficient online cluster scaling.
- Fault tolerance and high availability: Ensures high availability with automatic failure detection and recovery, maintaining consistency despite errors.
In conclusion, streaming databases are crucial for real-time data processing and analysis. Each tool offers unique features and capabilities, making them suitable for various use cases. When selecting a streaming database, consider your specific needs and requirements. These may include the type of data you'll be processing, the data volume, and your performance and reliability needs.
