GreptimeDB Glossary
Welcome to the GreptimeDB Glossary! This resource provides clear definitions and explanations of key terms and concepts associated with GreptimeDB, a cloud-native, open-source time-series database designed for metrics, logs, and events. Explore the glossary to better understand the innovative features and technologies behind GreptimeDB.
A
Anomaly Detection
The process of identifying data points, events, or observations that deviate significantly from the norm. In time-series data, anomaly detection helps in spotting unusual patterns that may indicate critical incidents.
C
Cardinality
A measure of the uniqueness of data elements in a database, such as the number of unique values in a column. High cardinality can increase the complexity and storage requirements of a database, especially in time-series data.
Cloud-Native Design
An architectural approach that utilizes cloud computing frameworks and services to build scalable and resilient applications. GreptimeDB's cloud-native design allows it to scale effortlessly from edge deployments to distributed clusters in the cloud.
Columnar Storage
A data storage format that stores data tables by columns rather than rows. This format enhances performance for read-heavy operations and is optimized for analytical queries, contributing to GreptimeDB's cost efficiency.
D
Decoupled Compute and Storage Architecture
An architectural design where computing resources and storage are managed separately. This separation enables independent scaling and resource optimization, leading to improved performance and flexibility in managing workloads.
E
Edge Database
A database deployed at the edge of a network, close to the data source or user, to minimize latency and optimize data processing in real-time.
Edge Deployment
The practice of deploying applications or services closer to the data source or end-user to reduce latency and bandwidth usage. GreptimeDB supports edge deployment, allowing for real-time data processing in resource-limited environments.
Event Management
The practice of collecting, organizing, and analyzing events—including metrics, logs, and traces—to monitor and optimize systems. Event management is a critical aspect of maintaining real-time systems and applications.
I
IoT Cloud
A cloud computing platform specifically designed to support Internet of Things (IoT) applications by providing the necessary storage, processing power, and connectivity to manage IoT data at scale.
IoT Database
A database optimized for handling Internet of Things (IoT) data, which often involves time-series metrics from sensors and devices. GreptimeDB is suitable for IoT use cases, providing scalable and efficient storage and querying for high-frequency data generated by IoT devices.
IoT Observability
The ability to monitor, analyze, and gain insights into IoT devices and systems through metrics, logs, and events. IoT observability ensures that devices and applications perform reliably and efficiently.
Interoperability
The ability of different systems, applications, or products to connect and communicate in a coordinated way without effort from the end-user. GreptimeDB supports widely adopted database protocols and APIs, including SQL, InfluxDB, OpenTelemetry, Prometheus, Elasticsearch, and Loki, ensuring seamless integration.
L
Log Aggregation
Perform calculations on a set of logs to generate a single summary statistic for analysis and troubleshooting. For example, SUM, COUNT, etc.
Log Management
The overall process of handling log data, including collection, storage, analysis, and visualization, to ensure system performance and security.
M
Memory Leak
A type of software bug where a program fails to release unused memory, causing a gradual decrease in available memory and potential system instability over time.
MetricsQL
An extension of PromQL (Prometheus Query Language) that introduces additional features for querying time-series data. MetricsQL enhances analytical capabilities, allowing for more complex queries and data manipulations.
O
Observability
A measure of how well the internal states of a system can be inferred based on its external outputs. Observability tools, such as GreptimeDB, help engineers monitor, debug, and gain insights into system performance by analyzing metrics, logs, and events.
OpenTelemetry
An open-source observability framework for cloud-native software. OpenTelemetry provides APIs and SDKs for collecting, processing, and exporting telemetry data such as traces, metrics, and logs. GreptimeDB integrates with OpenTelemetry to enhance data observability.
P
PromQL (Prometheus Query Language)
A powerful and flexible query language used to retrieve and manipulate time-series data stored in Prometheus. GreptimeDB supports PromQL, enabling users to perform complex queries on their time-series data.
R
Rust
A modern programming language known for its performance and safety features, particularly in system-level programming. GreptimeDB is built with Rust, contributing to its superior performance and reliability.
S
Scalability
The capability of a database system to handle growing volumes of data and increasing query loads efficiently by scaling resources either vertically (adding more power to a single server) or horizontally (adding more servers to a cluster). Scalability ensures that the system can accommodate future growth without sacrificing performance or reliability, making it crucial for modern data-intensive applications.
SQL (Structured Query Language)
A standardized programming language used for managing and manipulating relational databases. GreptimeDB supports SQL, allowing users to query metrics, logs, and events efficiently.
Streaming Processing
The continuous processing of data streams in real-time. GreptimeDB unifies metrics, logs, and events with native support for streaming processing, enabling real-time analytics and insights.
T
Time Series Database
A specialized database designed to handle time-series data, which consists of sequences of data points indexed by timestamps. GreptimeDB is a cloud-native time-series database optimized for analyzing and querying metrics, logs, and events.
U
Unified Analysis
The integration of various data types and sources into a single platform for analysis. GreptimeDB provides unified analysis by allowing users to query metrics, logs, and events using SQL and PromQL, simplifying data analytics workflows.
V
Vector Processing
A computational method that involves processing data as vectors (arrays of data) to achieve high-performance analytics. GreptimeDB supports vector processing for tasks such as similarity searches and high-dimensional data analysis in time-series and event data.
Vehicle Data Collection
The process of gathering data generated by vehicles, such as sensor readings, GPS locations, and diagnostics, for analysis and insights. Vehicle data collection is a key component of modern IoT ecosystems.
Vehicle-Cloud Integrated TSDB
A time-series database designed to work seamlessly with vehicle data and cloud-based systems, enabling efficient data storage, querying, and real-time analysis for connected vehicle applications.
Note: This glossary is a work in progress and will be updated as new features and concepts emerge within the GreptimeDB ecosystem.