Apache Accumulo

Apache Accumulo is a highly scalable, sorted, distributed key/value store built on top of Apache Hadoop’s HDFS. It is designed for robust and secure data storage and retrieval across large clusters. Inspired by Google’s BigTable, Accumulo supports fine-grained access control, efficient data compression, and server-side programming, making it ideal for applications requiring high performance, scalability, and security.

Features

• Sorted Key/Value Storage
• Data is stored as sorted key/value pairs, enabling fast range queries and efficient scans.
• Cell-Level Security
• Each key/value pair includes a visibility label for fine-grained access control based on user authorizations.
• Server-Side Programming with Iterators
• Custom logic can be applied during data scans, compactions, and other operations using iterators.
• Automatic Load Balancing and Partitioning
• Data is automatically distributed across tablet servers for optimal performance and scalability.
• Compression and Versioning
• Supports data compression and versioning through timestamped keys.
• Stable Client API
• Follows semantic versioning and long-term maintenance releases for reliable integration.

Capabilities

• Distributed Architecture
• Operates across clusters using HDFS and ZooKeeper for coordination and fault tolerance.
• Tablet Server Management
• Handles data ingestion, sorting, flushing to disk, and recovery from failures.
• MapReduce Integration
• Accumulo tables can be used as input/output for Hadoop MapReduce jobs.
• Garbage Collection
• Identifies and removes unused files to maintain storage efficiency.
• Master Server Coordination
• Oversees tablet server health and orchestrates recovery and load balancing.
• Mini Accumulo Cluster
• Enables local testing and development with a lightweight cluster setup.

Benefits

• Scalability
• Easily scales with data growth by adding nodes to the cluster.
• Security
• Fine-grained access control ensures data confidentiality and compliance.
• Performance
• Optimized for fast reads and writes with efficient memory and disk usage.
• Flexibility
• Supports custom data processing and integration with other big data tools.
• Reliability
• Built-in fault tolerance and recovery mechanisms ensure high availability.