DBMS > KurrentDB (aka EventStoreDB)
KurrentDB (aka EventStoreDB) System Properties
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| Name | KurrentDB (aka EventStoreDB) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Description | An event-driven database that records every change as an immutable event, indexes it for fast access via fine-grained streams, and projects it into state exposed via pub/sub or database tables for efficient event-driven workflows. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Primary database model | Event Store | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Secondary database models | Document store  Event data uses JSON per defaultTime Series DBMS Strict global and per-stream ordering of events allows for time-series use cases requiring auditability and immutability | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Website | www.kurrent.io | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Technical documentation | docs.kurrent.io | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Developer | Kurrent, Inc. (aka EventStoreDB) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Initial release | 2012 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Current release | 25.1, October 2025 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| License Commercial or Open Source | Source Available | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Cloud-based only Only available as a cloud service | no Self-Managed: On-Prem and Cloud, and As cloud-based DBaaS (Kurrent Cloud) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| DBaaS offerings (sponsored links) Database as a Service Providers of DBaaS offerings, please contact us to be listed. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Implementation language | .NET/C# | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Server operating systems | Docker Kubernetes Linux Windows | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data scheme | schema-free | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Typing predefined data types such as float or date | Yes: JSON event data and metadata; Binary event data; Strongly-typed language interfaces | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| XML support Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT. | Partial: Store XML as string data; Convert XML to JSON; Base64 encode XML Client SDKs in .NET, Java, Python, Node.js, Go, and Rust provide flexibility to handle XML serialization/deserialization in application code before sending events to KurrentDB | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Secondary indexes | Two virtual index streams built on the most frequently queried metadata fields. Consumers use them by pointing their stream filter to the index stream via the Read/Subscription API. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| SQL Support of SQL | Introspective SQL for simplified metrics collection of system, database, and application operations | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| APIs and other access methods | Archiving Archive old data to object storage for long-term retention, reconstruction and DR Client SDKs Available in multiple languages including Python, Java, .NET, Node.js, Go, and RustgRPC API High performance, low latency interface with streaming support for all KurrentDB operationsHTTP API RESTful interface for basic administration and operationsManagement API For administrative operationsMCP Server conversational interactions with events for both technical and non-technical usersNavigator Desktop client interface for managing events, streams, configurations, and monitoringOpenTelemetry Exporter Push metrics via OpenTelemetry protocolPrometheus Metrics Export metrics in Prometheus formatPub / Sub API Catch-up Subscriptions - Self-managed subscriptions for filtered events by stream or event type, and Persistent Subscriptions - Server-managed subscriptions with multiple consumers, checkpointing, and retriesWeb UI Browser-based client interface for managing events, streams, configurations, and monitoring | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Supported programming languages | .NET/C# Go Java Node.js Python Rust | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Server-side scripts Stored procedures | User-Defined Projections, System Projections, Projection Configuration Options, Server Administration Scripts, Connector Transformations | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Triggers | Projections, Persistent Subscriptions, Catch-up Subscriptions, Connectors | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Partitioning methods Methods for storing different data on different nodes | Stream Level Partitioning, where each stream holds the events for a single entity or process (e.g., customer-123, order-456); Stream boundaries that align with business aggregates / entities and transactional consistency requirements; Fine-grained vs. coarse-grained streams - KurrentDB promotes fine-grained streams without expensive re-partitioning; Category-based organization where streams can be grouped by categories for easier management; Support for billions of streams, so reads/writes touch only the relevant stream, improving latency, scalability, and maintenance; For downstream pipelines (e.g., Kafka), partitions can be aligned by stream ID, suffix, or event headers, keeping physical partitioning consistent with KurrentDB’s logical model | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Replication methods Methods for redundantly storing data on multiple nodes | Built-in Clustering Replication HA Clustering: Uses secure, quorum-based replication with leader election via gossip protocol, where each node maintains data independently without shared disks Read-only Replica - Nodes that replicate data asynchronously from the cluster but don't participate in write operations, elections, or quorum - primarily used to scale read operations Event Distribution Methods Catch-up Subscriptions - Self-managed subscriptions that can replicate filtered events by stream or event type from a particular position Persistent Subscriptions - Server-managed subscriptions supporting multiple competing consumers with checkpointing and retriesExternal System Replication Connector System - Real-time event distribution to external systems with at-least-once delivery, retry capabilities, event filtering, and automatic checkpointing Multi-sink Support - Can replicate to KurrentDB, Kafka, RabbitMQ, MongoDB, and HTTP endpoints simultaneouslyKurrent Replicator Cross-cluster Replication - A specialized tool that facilitates replication or migration of data between different KurrentDB clusters or instances Migration Support - Used specifically for migrating data between clusters, allowing you to replicate all events from one cluster to another | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| MapReduce Offers an API for user-defined Map/Reduce methods | Event-native processing and streaming; Data Pipeline Integration; Distributed Processing Patterns; KurrentDB offers event-native processing and streaming: User-Defined Projections - JavaScript-based server-side processing for transformations and state aggregations across streams; System Projections - Built-in indexing by category, event type, and correlation ID for efficient event retrieval; Data Pipeline Integration: Native Connectors - Real-time event streaming to external systems where MapReduce processing could occur (e.g. Spark/Hadoop via Kafka or files via HTTP); Event Export - Stream events to data processing platforms that do support MapReduce; Distributed Processing Patterns: Stream Processing - Events can be processed in parallel across multiple subscribers; Event Replication - Distribute events across multiple systems for parallel processing | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Consistency concepts Methods to ensure consistency in a distributed system | Append-only Log - Events are only appended and never modified, serving as an immutable audit trail of all historical changes Atomic writes of events - entire batch of events submitted in the same write. Eventual Consistency - KurrentDB supports eventually consistent systems where all parts of a distributed system will eventually return the same values, typically within milliseconds to seconds rather than large spans of time Guaranteed Consistent Ordering - Events are consistently ordered in the order they were appended across both the event log and streams, ensuring events are always read in the same consistent order Guaranteed Write Durability - Writes are guaranteed to be fully durable once acknowledged Immutable Events - All events cannot be altered once appended, ensuring data integrity and consistent audit trails Optimistic Concurrency Control - Prevents lost updates from concurrent appends using a lock-free approach to reduce contention and performance overhead Quorum-based Replication - High availability clusters use secure, quorum-based replication with leader election via gossip protocol Read-only Replicas - Asynchronous replication for scaling read operations without participating in write operations or quorum decisions Sequential Event Numbering - Events are automatically assigned strictly monotonically increasing numbers (without gaps) within their stream to ensure reliable state reconstruction and concurrency handling Stream-level Consistency - KurrentDB can ensure data consistency within a stream when multiple writers are trying to append concurrently Transactional vs Eventual Consistency - The system allows you to consciously choose consistency boundaries so business requirements are met, with everything else being eventually consistent | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Foreign keys Referential integrity | None, but Kurrent DB provides other event-native consistency guarantees: Correlation IDs - Events can include correlation IDs to link related events across different streams; Event-driven associations - Use projections to build read models that represent relationships between entities; Application-level validation - Business logic in your application ensures data integrity rather than database constraints; Event sourcing patterns - Model relationships through the sequence and content of events rather than foreign key constraints; Optimistic concurrency control - Prevents lost updates during concurrent writes; Sequential event numbering - Ensures reliable state reconstruction; Immutable events - Once written, events cannot be altered, providing audit trail integrity | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Transaction concepts Support to ensure data integrity after non-atomic manipulations of data | Writes use an implicit transaction: all records are appended contiguously in the same chunk and committed atomically. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Concurrency Support for concurrent manipulation of data | Traditional databases rely on pessimistic locking, KurrentDB's event-driven architecture with optimistic concurrency control provides better scalability while maintaining data consistency through its append-only, immutable event model; Optimistic Concurrency Control: Lock-free approach - Concurrent appends that lead to lost updates can be prevented with optimistic concurrency control, done in a lock-free way to reduce contention and performance overhead; Stream-level consistency - KurrentDB can ensure data consistency within a stream when multiple writers are trying to append concurrently; Sequential event numbering - Events are automatically assigned strictly monotonically increasing numbers (without gaps) to ensure reliable state reconstruction and concurrency handling; Stream Position Management: Unique event positions - Each event has its own unique position within a stream, represented by a numeric, incremental value that can be used to define order and detect concurrency issues; Race condition prevention - The position system helps prevent accidental overwrites or lost updates due to concurrency and race conditions; Distributed Concurrency: Quorum-based clustering - High availability clusters use secure, quorum-based replication with leader election via gossip protocol for distributed concurrency management; Multiple competing consumers - Persistent subscriptions support multiple competing consumers with checkpointing and retries | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Durability Support for making data persistent | Core Durability Features: Guaranteed Write Durability - Writes are guaranteed to be fully durable once acknowledged, ensuring data is safely persisted to disk; Immutable Events - All events stored in KurrentDB cannot be altered once appended, ensuring data integrity and durability over time; Append-only Log - Events are only appended and never modified, serving as an immutable audit trail that captures all historical changes; Storage and Backup Durability: Backup and Restore - Comprehensive backup functionality for full or differential backups and restores over disk snapshots or file copy backups; Event Store Replicator - Tool for facilitating replication or migration of data between different KurrentDB clusters to ensure data durability across environments; Archiving / DR - Archive to ObjectStore or long-term data retention; Clustering for High Durability: High Availability Clustering - Uses secure, quorum-based replication with leader election via gossip protocol, where each node independently maintains data without shared disks; Read-only Replicas - Asynchronous replication to additional nodes for enhanced data durability and read scaling | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| User concepts Access control | KMS Integration (for connectors) Kubernetes Operator Role-based access controls LDAP integration Mutual TLS / X.509 authentication OAuth authentication Password-based authentication Role-Based Access Control Stream Access Policy Stream ACLs | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| KurrentDB (aka EventStoreDB) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Recent citations in the news | Event Store secures Series A investment from Qualasept Holdings provided by Google News | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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