What does apache/iotdb do?

Apache IoTDB is a time-series database designed for the Internet of Things, purpose-built to ingest high-volume data from millions of low-power devices and store timestamp-value pairs with configurable data types and encoding schemes. It organizes time series data and device metadata in a tree-like hierarchy, enabling efficient management of complex industrial sensor networks.

What are the main features of apache/iotdb?

The main features of apache/iotdb are: Time Series Data Storage, Big Data Processing, High-Throughput Ingestion Pipelines, High-Volume Data Ingestion, Time-Series Aggregations, Time-Series Query Analytics, Time-Series SQL Querying, Hadoop Integrations.

What are some open-source alternatives to apache/iotdb?

Open-source alternatives to apache/iotdb include: opentsdb/opentsdb — OpenTSDB is a distributed time series database and metrics engine designed for storing and managing massive volumes of… questdb/questdb — QuestDB is a high-performance, distributed time-series database designed for the ingestion, storage, and analysis of… greptimeteam/greptimedb — GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries… influxdb/influxdb — InfluxDB is a high-performance time-series database designed for collecting, storing, and querying time-stamped… m3db/m3 — m3 is a distributed time series database designed for high-resolution metrics and high-cardinality data management. It… awslabs/gluonts — GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for…

Iotdb | Awesome Repos

Features

Time Series Data Storage - Collects and persists timestamp-value pairs from industrial devices with configurable data types and encoding schemes.
Big Data Processing - Connect to Hadoop, Spark, Flink, and Grafana to process, analyze, and visualize time-series data.
High-Throughput Ingestion Pipelines - Ingest and retrieve data from millions of concurrent device connections at high throughput.
High-Volume Data Ingestion - Accepts writes from millions of low-power devices at high speed, enabling real-time data collection from industrial sensors.
Time-Series Aggregations - Joins time-series data from multiple devices and sensors, and performs time-based aggregation like downsampling.
Time-Series Query Analytics - Aligns data across devices, transforms signals in the frequency domain, and aggregates along time dimensions.
Time-Series SQL Querying - Retrieves stored time series by path, supports filtering by time range and selecting multiple series in a single query.
Hadoop Integrations - Connect with Hadoop, Spark, and Grafana to analyze and visualize time-series data.
Time-Series Compression - Compress time series data with high-ratio algorithms to reduce hardware storage costs.
Edge-to-Cloud Synchronization - Installs with one click on cloud platforms, provides terminal access tools, and synchronizes data between edge and cloud nodes.
Time Series Data Loading - Loads time series data from external files into the database for storage and analysis.
Time Series Export - Writes stored time series data to external files for backup or transfer to other systems.
Hierarchical Time Series Organizations - Structures time series data in a tree-like directory for efficient management of complex device and measurement hierarchies.
Flexible - Installs on cloud platforms or terminal devices with one-click setup and synchronizes data between them.
IoT Device Tree Hierarchies - Manages IoT device metadata as a tree structure and supports wildcard-based fuzzy matching for flexible queries.
Data Processing - Provides frequency-based encoding and visualization sampling algorithms.

Open-Source-Alternativen zu Iotdb

Ähnliche Open-Source-Projekte, sortiert nach der Anzahl der gemeinsamen Funktionen mit Iotdb.

opentsdb/opentsdb
OpenTSDB/opentsdb
5,068Auf GitHub ansehen
OpenTSDB is a distributed time series database and metrics engine designed for storing and managing massive volumes of high-cardinality system metrics. It functions as a data store and analytics platform that enables large-scale metric ingestion and infrastructure performance monitoring across a distributed cluster. The system distinguishes itself through a distributed storage abstraction that supports multiple backends such as HBase, Cassandra, and Google Bigtable. It utilizes a hierarchical metric tree to organize time series and employs numeric identifier indexing to reduce storage footpri
Java
Auf GitHub ansehen5,068
questdb/questdb
questdb/questdb
17,062Auf GitHub ansehen
QuestDB is a high-performance, distributed time-series database designed for the ingestion, storage, and analysis of massive datasets. It functions as a real-time analytics platform that utilizes a columnar storage engine to optimize disk input and output, enabling efficient analytical scans and complex windowing operations on streaming data. The platform distinguishes itself through specialized capabilities for handling asynchronous time-series streams, including advanced join algorithms that align disparate data sets based on precise timestamp lookups. It supports high-volume ingestion thro
Javacapital-marketscppdatabase
Auf GitHub ansehen17,062
greptimeteam/greptimedb
GreptimeTeam/greptimedb
5,968Auf GitHub ansehen
GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries metrics, logs, and traces together in a single columnar engine, supporting both SQL and PromQL for analysis. The database is designed as a Kubernetes-native operator with a decoupled compute and storage architecture, enabling horizontal scaling and multi-region deployment. What distinguishes GreptimeDB is its role as a multi-protocol ingestion gateway, accepting data through OpenTelemetry, Prometheus Remote Write, InfluxDB, Loki, Elasticsearch, Kafka, and MQTT protocols without
Rustanalyticscloud-nativedatabase
Auf GitHub ansehen5,968
influxdb/influxdb
influxdb/influxdb
31,557Auf GitHub ansehen
InfluxDB is a high-performance time-series database designed for collecting, storing, and querying time-stamped metrics and event data. It functions as a columnar time-series store and a real-time analytics engine, providing a network-accessible interface for retrieving and analyzing temporal records. The system utilizes a specialized columnar storage format to support high ingestion rates and efficient data retrieval. It incorporates a programmable runtime for executing custom plugins and triggers, including integration for processing and transforming incoming data streams. The platform cov
Rust
Auf GitHub ansehen31,557

Alle 30 Alternativen zu Iotdb anzeigen

apacheiotdb

Iotdb

Features

Open-Source-Alternativen zu Iotdb

OpenTSDB/opentsdb

questdb/questdb

GreptimeTeam/greptimedb

influxdb/influxdb

Frequently asked questions

Star-Verlauf

Open-Source-Alternativen zu Iotdb

OpenTSDB/opentsdb

questdb/questdb

GreptimeTeam/greptimedb

influxdb/influxdb

Frequently asked questions