ydb-platform/ydb

Features

• Analytical Data Engines - Processes petabyte-scale analytical queries using a vectorized query engine and columnar storage.
• Distributed SQL Databases - Provides a distributed SQL database architecture that combines horizontal scalability with strong transactional consistency.
• ACID Transaction Semantics - Guarantees serializable ACID transactions for strict data consistency across distributed nodes.
• Analytical Query Engines - Scales analytical workloads to petabytes using a vectorized query engine and distributed execution planner.
• Hybrid Storage Formats - Maintains data in both row and columnar formats to optimize for both transactional and analytical workloads.
• Cross-Zone Data Distribution - Distributes data across multiple availability zones to ensure access during simultaneous zone and rack failures.
• Data Recovery - Automatically restores data redundancy and service availability following disk, node, or datacenter failures.
• Declarative Query Languages - Provides a structured query language for declarative data retrieval and manipulation across a distributed cluster.
• Distributed Sharding Architectures - Implements distributed tablet sharding to enable horizontal scaling and balanced load distribution across nodes.
• Horizontal Scaling - Enables horizontal resource scaling by adding storage and computation nodes to the cluster.
• PostgreSQL-Compatible Databases - Implements the PostgreSQL wire protocol and SQL dialect for compatibility with standard database drivers.
• Real-Time Data Streaming - Integrates database tables with Kafka topics and processes unbounded data streams via change data capture.
• SQL Query Dialects - Provides a structured query language dialect for precise data management and retrieval.
• Vector Databases - Serves as a vector database supporting vector indexes and approximate nearest neighbor searches for embeddings.
• Vectorized Execution Engines - Utilizes a vectorized execution engine to process data in batches for high-performance analytical workloads.
• Column-Oriented Disk Storage - Organizes data independently by column to optimize analytical aggregates and maximize compression ratios.
• Distributed Data Workload Scaling - Distributes data and workloads across multiple nodes to ensure horizontal scalability and high availability.
• Hash-Based Data Distribution - Uses hash-based partitioning of keys to distribute data across shards and prevent insertion hotspots.
• Shard Rebalancing - Supports online addition or removal of nodes with automatic sharding to adapt to changing workloads.
• Backup & Recovery - Creates point-in-time database copies on object stores or filesystems for disaster recovery.
• Change Data Capture Streams - Provides real-time change data capture streaming of row-level modifications for external downstream processing.
• Erasure Coding - Uses erasure coding to provide high fault tolerance and data availability with lower storage overhead than mirroring.
• Tabular Row Storage - Stores data sequentially by row to optimize for transactional queries and fast single-record access.
• Database Wire Protocol Emulations - Implements various database wire protocols to allow standard drivers to interact with the internal system.
• Distributed Query Stream Processors - Runs streaming queries that automatically restart on failure and use checkpoints to persist state.
• Federated Data Query Engines - Executes federated queries to retrieve and process data from multiple external sources without migration.
• Indexing and Search - Supports a combination of primary, secondary, and vector indexes to optimize diverse data retrieval patterns.
• Multi-Tenant Resource Isolation - Runs multiple isolated databases within a single cluster using a shared storage layer to partition resources.
• PostgreSQL Protocol Compatibility - Supports the standard PostgreSQL wire protocol to ensure compatibility with existing PostgreSQL database drivers.
• Transactional Delivery Guarantees - Moves data between messaging topics and tables using transactional guarantees to ensure reliability.
• Vector Similarity Search - Executes approximate k-nearest neighbor queries on embeddings for semantic search and recommendations.
• Database Deployment Automation - Automates the installation and configuration of distributed database clusters across servers using orchestration tools.
• Operator-Managed Deployments - Employs a Kubernetes operator to automate the installation and lifecycle of storage and dynamic nodes.
• Database Cluster Deployments - Provides specialized orchestration for deploying and scaling distributed database clusters within container environments.
• Database Deployments - Offers automated deployment and management of database clusters in Kubernetes using a dedicated operator.
• Kafka Protocol Implementations - Implements the Kafka wire protocol to enable seamless integration with standard Kafka clients and topics.
• Database Protocol Implementations - Integrates with applications using the Kafka protocol for seamless messaging and streaming within the database.
• Persistent Queues - Provides persistent messaging queues with exactly-once delivery and automatic partitioning.
• Dynamic Shard Splitting - Automatically splits table partitions based on data size or request load to balance traffic across nodes.

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