OpenTSDB/opentsdb

Features

• Time Series Data Storage - Stores individual data points consisting of a metric name, timestamp, value, and associated tags.
• Distributed Metrics Stores - Functions as a distributed metrics store that shards and replicates high-cardinality time-series data across clusters.
• Time-Series Downsamplers - Reduces the resolution of high-frequency time series data to optimize historical query performance.
• Data Write Throughput Optimizers - Scales write throughput by distributing incoming data points across a cluster of nodes to handle millions of points per second.
• Metric Ingestion APIs - Provides a scalable API for ingesting single or batched system metric data points.
• Distributed Query Fragmenters - Splits complex analytical queries into parallel fragments to be executed across multiple cluster nodes.
• Distributed Query Processing - Parallelizes complex query requests across multiple nodes to reduce response latency.
• External Storage Integrations - Connects to various backend storage engines such as HBase, Google Bigtable, or Cassandra.
• Quantile Digest Aggregators - Computes accurate distribution quantiles across multiple data points using histograms, digests, and sketches.
• Metric Metadata Trees - Organizes time series into a navigable tree structure using metadata rules to group metrics by tags.
• High-Precision Time Series Storage - Writes high-cardinality system metrics to a distributed store with millisecond precision.
• Metric Storage Scaling - Scales metric storage horizontally to handle millions of incoming data points per second using distributed write loads.
• Metadata Caches - Maintains a specialized in-memory cache for time series metadata to reduce the latency of repetitive lookups.
• Metadata Caching - Optimizes retrieval speed by caching frequently accessed metadata and pre-populating identifier caches.
• Write Load Distribution - Prevents server hotspots by prepending salt bytes to identifiers to distribute write loads across the cluster.
• Metric Identifier Indexing - Maps high-cardinality metric names and tags to unique numeric IDs to reduce storage and accelerate lookups.
• Large Scale Indexing - Indexes high-frequency time series data from numerous hosts at scale without requiring record deletion or downsampling.
• Time-Series Extractions - Implements tag-based retrieval of time-indexed metrics via API, CLI, and visualization tools.
• Time-Series Aggregations - Merges multiple data points for a single timestamp into a single value using mathematical aggregation functions.
• Time Series Databases - Provides a scalable time-series database supporting distributed backends like HBase, Cassandra, and Bigtable.
• Metric Aggregation & Downsampling - Aggregates high-resolution metrics into lower-resolution time-based buckets to optimize long-term storage and query performance.
• Time Series Indexing - Uses generic metric names with key-value pairs to enable fast aggregations across large datasets.
• Hierarchical Time Series Organizations - Organizes time series into a navigable, hierarchical tree structure based on tag-grouping rules.
• Identifier Mapping Caches - Maps high-cardinality metric names and tags to unique binary identifiers to optimize storage and lookup speed.
• Storage Abstraction Layers - Decouples the data layer using a plugin system to support multiple backends like HBase and Cassandra.
• Data Ingestion Plugins - Provides a modular plugin system to ingest metrics from diverse sources using various serializers and protocols.
• Distributed Metric Ingestion - Ingests massive volumes of system metrics from numerous hosts using a distributed database architecture.
• Hierarchical Metadata Managers - Organizes time series into navigable hierarchies and manages unique identifiers for tagged metrics.
• System Metrics Collection - Gathers performance data from hosts and applications and pushes it into the distributed time series store.
• Real-time Performance Monitoring - Collects and visualizes real-time performance metrics from hosts and applications to monitor system health.
• Scalable Metrics Infrastructure - Implements a distributed infrastructure designed to handle massive ingestion and query loads for telemetry.
• Metric Storage & Querying - Provides a comprehensive system for storing and querying time-series performance data.
• Spatial Pre-Aggregation - Stores the spatial aggregate of multiple time series into a single series to accelerate queries on high-cardinality data.
• Write Load Distribution - Prevents database hotspots by prepending random bytes to identifiers to evenly distribute write loads across the cluster.
• Write-Optimized Storage Engines - Arranges data based on timestamps to reduce disk reads and minimize background merge overhead.
• Time-Series Compression - Merges multiple columns within a row into a single column to reduce the physical disk space usage.
• Histogram Data Storage - Writes histogram data points to storage to track measurement distributions over specific time intervals.
• Distributed Percentile Estimation - Computes accurate percentiles across distributed data sources using histograms and sketches.
• Metric Collector Integrations - Accepts data streams from various external system monitors and agents to centralize metric storage.
• Metadata Indexing - Integrates with external search engines by indexing metadata and annotations for complex querying of time series.
• Historical Data Import - Loads historical data from external systems or backfills records using a command-line utility.
• Metric Metadata Assignment - Stores descriptions and key-value pairs for individual metrics to provide necessary context for unique identifiers.
• Metric Data Summarizations - Summarizes high-resolution data into lower-resolution stored versions to accelerate queries and reduce storage footprint.
• Metric Hierarchy Construction - Deno X processes new metadata objects through configured rules immediately to update the hierarchical structure.
• Metric Statistics Querying - Retrieves aggregated data and statistical summaries through a telnet-style command interface.
• Block Caches - Utilizes block-level caching and off-heap memory to minimize disk I/O during data write and retrieval processes.
• Storage Access Control - Provides secure, authenticated, and encrypted access to the distributed storage cluster using Kerberos.
• CLI Metric Queries - Enables querying time series and importing data via a command-line interface for consistency checks.
• System Metadata Tracking - Records creation timestamps and descriptive notes for specific combinations of metrics and tags.
• Time Series Analysis - Performs mathematical aggregations and calculates distributed percentiles to analyze trends in historical time-series data.
• Time-Series Query Analytics - Enables complex time-series analytics including distributed percentiles and mathematical aggregations.
• Metric Hierarchy Navigation - Enables navigation of hierarchical tree structures via HTTP to discover specific time series.
• Metric Identifier Management - Provides the ability to rename or delete unique identifiers for metrics and tags to maintain accurate metadata.
• Database CLI Administration Tools - Offers command-line utility tools for managing system configurations and performing administrative database tasks.
• Daemon Process Management - Provides administrative control over the central server process for handling remote procedure calls.
• Metric Ingestion Protocols - Supports custom binary communication protocols like Protobufs and Thrift for efficient metric ingestion.
• Read-Write Queue Separation - Separates read and write request queues to prevent large queries from blocking data ingestion.
• Off-Heap Memory Managers - Implements off-heap block caching to minimize Java garbage collection pauses and disk I/O.
• Rate of Change Calculations - Transforms increasing counters into per-second rates of change while correctly handling counter rollovers.
• API Request Authentication - Verifies client identity through authentication processes before granting access to the metrics API.
• Time-Series Function Pipelines - Applies arithmetic operations and group-by functions to time-series data during the query process.
• Metric Threshold Alerting - Monitors time series data and triggers notifications when metrics cross predefined numerical thresholds.
• Ingestion Filters - Filters incoming time series data using whitelists to determine which metrics are allowed into the system.
• Metric Ingestion Control - Implements logic to determine whether new metrics and tags are automatically assigned identifiers or rejected upon arrival.
• Metric Time-Series Graphs - Generates graphs from stored metrics via a web interface to visualize performance trends and bottlenecks over time.
• Time-Series Export APIs - Enables programmatic retrieval of time series data for statistical computing and custom visualization.
• Database Systems - Scalable distributed time-series database.
• Databases and Storage - Scalable database for time-series data.
• Distributed Databases - Scalable store for massive amounts of time series data.
• Monitoring Backends - Distributed time-series database built on top of storage clusters.
• Relational Databases - Scalable and distributed time-series database.
• Time Series Databases - Scalable time series database built on HBase.
• Backend and Infrastructure - Scalable time-series database for metrics.

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