| title | excerpt | tags | products | |||||
|---|---|---|---|---|---|---|---|---|
Tiger Data glossary of terms |
Find definitions of the key terms used in Tiger Data documentation, real-time analytics, and database industry |
|
|
This glossary defines technical terms, concepts, and terminology used in $COMPANY documentation, database industry, and real-time analytics.
ACL (Access Control List): a table that tells a computer operating system which access rights each user has to a particular system object, such as a file directory or individual file.
ACID: a set of properties (atomicity, consistency, isolation, durability) that guarantee database transactions are processed reliably.
ACID compliance: a set of database properties—Atomicity, Consistency, Isolation, Durability—ensuring reliable and consistent transactions. Inherited from $PG.
Adaptive query optimization: dynamic query plan adjustment based on actual execution statistics and data distribution patterns, improving performance over time.
Agent: a system that uses prompts and other logic to act autonomously or semi-autonomously.
AI agent: See Agent.
Aggregate (Continuous Aggregate): a materialized, precomputed summary of query results over time-series data, providing faster access to analytics.
Alerting: the process of automatically notifying administrators when predefined conditions or thresholds are met in system monitoring.
Analytics database: a system optimized for large-scale analytical queries, supporting complex aggregations, time-based queries, and data exploration.
Anomaly detection: the identification of abnormal patterns or outliers within time-series datasets, common in observability, IoT, and finance.
Append-only storage: a storage pattern where data is only added, never modified in place. Ideal for time-series workloads and audit trails.
Archival: the process of moving old or infrequently accessed data to long-term, cost-effective storage solutions.
Auto-partitioning: automatic division of a $HYPERTABLE into chunks based on partitioning dimensions to optimize scalability and performance.
Availability zone: an isolated location within a cloud region that provides redundant power, networking, and connectivity.
B-tree: a self-balancing tree data structure that maintains sorted data and allows searches, sequential access, insertions, and deletions in logarithmic time.
Background job: an automated task that runs in the background without user intervention, typically for maintenance operations like compression or data retention.
Background worker: a $PG process that runs background tasks independently of client sessions.
Batch processing: handling data in grouped batches rather than as individual real-time events, often used for historical data processing.
Backfill: the process of filling in historical data that was missing or needs to be recalculated, often used during migrations or after schema changes.
Backup: a copy of data stored separately from the original data to protect against data loss, corruption, or system failure.
Bloom filter: a probabilistic data structure that tests set membership with possible false positives but no false negatives. $TIMESCALE_DB uses blocked bloom filters to speed up point lookups by eliminating chunks that don't contain queried values.
Buffer pool: memory area where frequently accessed data pages are cached to reduce disk I/O operations.
BRIN (Block Range Index): a $PG index type that stores summaries about ranges of table blocks, useful for large tables with naturally ordered data.
Bytea: a $PG data type for storing binary data as a sequence of bytes.
Cache hit ratio: the percentage of data requests served from memory cache rather than disk, indicating query performance efficiency.
Cardinality: the number of unique values in a dataset or database column.
Check constraint: a database constraint that limits the values that can be stored in a column by checking them against a specified condition.
Chunk: a horizontal partition of a $HYPERTABLE that contains data for a specific time interval and space partition. See chunks.
Chunk interval: the time period covered by each chunk in a $HYPERTABLE, which affects query performance and storage efficiency.
Chunk skipping: a query optimization technique that skips chunks not relevant to the query's time range, dramatically improving performance.
CIDR (Classless Inter-Domain Routing): a method for allocating IP addresses and routing IP packets.
Client credentials: authentication tokens used by applications to access services programmatically without user interaction.
Close: in financial data, the closing price of a security at the end of a trading period.
Cloud: computing services delivered over the internet, including servers, storage, databases, networking, software, analytics, and intelligence.
Cloud deployment: the use of public, private, or hybrid cloud infrastructure to host $TIMESCALE_DB, enabling elastic scalability and managed services.
Cloud-native: an approach to building applications that leverage cloud infrastructure, scalability, and services like Kubernetes.
Cold storage: a tier of data storage for infrequently accessed data that offers lower costs but higher access times.
Columnar: a data storage format that stores data column by column rather than row by row, optimizing for analytical queries.
Columnstore: $TIMESCALE_DB's columnar storage engine optimized for analytical workloads and compression.
Compression: the process of reducing data size by encoding information using fewer bits, improving storage efficiency and query performance. See compression.
Connection pooling: a technique for managing multiple database connections efficiently, reducing overhead for high-concurrency environments.
Consensus algorithm: protocols ensuring distributed systems agree on data state, critical for multi-node database deployments.
Compression policy: an automated rule that compresses $HYPERTABLE chunks after they reach a specified age or size threshold.
Compression ratio: the ratio between the original data size and the compressed data size, indicating compression effectiveness.
Constraint: a rule enforced by the database to maintain data integrity and consistency.
Continuous aggregate: a materialized view that incrementally updates with new data, providing fast access to pre-computed aggregations. See continuous aggregates.
Counter aggregation: aggregating monotonic counter data, handling counter resets and extrapolation.
Cron: a time-based job scheduler in Unix-like computer operating systems.
Cross-region backup: a backup stored in a different geographical region from the primary data for disaster recovery.
Data lake: a centralized repository storing structured and unstructured data at scale, often integrated with time-series databases for analytics.
Data lineage: the tracking of data flow from source to destination, including transformations, essential for compliance and debugging.
Data pipeline: automated workflows for moving, transforming, and loading data between systems, often using tools like Apache Kafka or Apache Airflow.
Data migration: the process of moving data from one system, storage type, or format to another. See the migration guides.
Data retention: the practice of storing data for a specified period before deletion, often governed by compliance requirements or storage optimization. See data retention.
Data rollup: the process of summarizing detailed historical data into higher-level aggregates, balancing storage needs with query efficiency.
Data skew: uneven distribution of data across partitions or nodes, potentially causing performance bottlenecks.
Data tiering: a storage management strategy that places data on different storage tiers based on access patterns and performance requirements.
Data type: a classification that specifies which type of value a variable can hold, such as integer, string, or boolean.
Decompress: the process of restoring compressed data to its original, uncompressed state.
Delta: the difference between two values, commonly used in counter aggregations to calculate the change over time.
DHCP (Dynamic Host Configuration Protocol): a network management protocol used to automatically assign IP addresses and other network configuration parameters.
Dimension: a partitioning key in a $HYPERTABLE that determines how data is distributed across chunks.
Disaster recovery: the process and procedures for recovering and protecting a business's IT infrastructure in the event of a disaster.
Double precision: a floating-point data type that provides more precision than the standard float type.
Downsample: the process of reducing the temporal resolution of time-series data by aggregating data points over longer time intervals.
Downtime: the period during which a system, service, or application is unavailable or not operational.
Dual-write and backfill: a migration approach where new data is written to both the source and target databases simultaneously, followed by backfilling historical data to ensure completeness.
Dual-write: a migration pattern where applications write data to both the source and target systems simultaneously.
Edge computing: processing data at or near the data source such as IoT devices, rather than solely in centralized servers, reducing latency.
Edge gateway: a device that aggregates data from sensors and performs preprocessing before sending data to cloud or centralized databases.
ELT (Extract, Load, Transform): a data pipeline pattern where raw data is loaded first, then transformed within the target system, leveraging database processing power.
Embedding: a vector representation of data such as text or images, that captures semantic meaning in a high-dimensional space.
Error rate: the percentage of requests or operations that result in errors over a given time period.
Euclidean distance: a measure of the straight-line distance between two points in multidimensional space.
Exactly-once: a message is delivered and processed precisely once. There is no loss and no duplicates.
Explain: a $PG command that shows the execution plan for a query, useful for performance analysis.
Event sourcing: an architectural pattern storing all changes as a sequence of events, naturally fitting time-series database capabilities.
Event-driven architecture: a design pattern where components react to events such as sensor readings, requiring real-time data pipelines and storage.
Extension: a $PG add-on that extends the database's functionality beyond the core features.
Fact table: the central table in a star schema containing quantitative measures, often time-series data with foreign keys to dimension tables.
Failover: the automatic switching to a backup system, server, or network upon the failure or abnormal termination of the primary system.
Financial time-series: high-volume, timestamped datasets like stock market feeds or trade logs, requiring low-latency, scalable databases like $TIMESCALE_DB.
Foreign key: a database constraint that establishes a link between data in two tables by referencing the primary key of another table.
Fork: a copy of a database service that shares the same data but can diverge independently through separate writes.
Free $SERVICE_SHORT: a free instance of $CLOUD_LONG with limited resources. You can create up to two free $SERVICE_SHORTs under any $PRICING_PLAN. When a free $SERVICE_SHORT reaches the resource limit, it converts to the read-only state. You can convert a free $SERVICE_SHORT to a standard one under paid $PRICING_PLANs.
FTP (File Transfer Protocol): a standard network protocol used for transferring files between a client and server on a computer network.
Gap filling: a technique for handling missing data points in time-series by interpolation or other methods, often implemented with hyperfunctions.
GIN (Generalized Inverted Index): a $PG index type designed for indexing composite values and supporting fast searches.
GiST (Generalized Search Tree): a $PG index type that provides a framework for implementing custom index types.
GP-LTTB: an advanced downsampling algorithm that extends Largest-Triangle-Three-Buckets with Gaussian Process modeling.
GUC (Grand Unified Configuration): $PG's configuration parameter system that controls various aspects of database behavior.
GUID (Globally Unique Identifier): a unique identifier used in software applications, typically represented as a 128-bit value.
Hash: an index type that provides constant-time lookups for equality comparisons but doesn't support range queries.
High-cardinality: refers to datasets with a large number of unique values, which can strain storage and indexing in time-series applications.
Histogram bucket: a predefined range of metrics organized for statistical analysis, commonly visualized in monitoring tools.
Hot standby: a replication configuration where the standby server can serve read-only queries while staying synchronized with the primary.
High availability: a system design that ensures an agreed level of operational performance, usually uptime, for a higher than normal period.
High: in financial data, the highest price of a security during a specific time period.
Histogram: a graphical representation of the distribution of numerical data, showing the frequency of data points in different ranges.
Historical data: previously recorded data that provides context and trends for analysis and decision-making.
HNSW (Hierarchical Navigable Small World): a graph-based algorithm for approximate nearest neighbor search in high-dimensional spaces.
Hot storage: a tier of data storage for frequently accessed data that provides the fastest access times but at higher cost.
Hypercore: $TIMESCALE_DB's hybrid storage engine that seamlessly combines row and column storage for optimal performance. See Hypercore.
Hyperfunction: an SQL function in $TIMESCALE_DB designed for time-series analysis, statistics, and specialized computations. See Hyperfunctions.
HyperLogLog: a probabilistic data structure used for estimating the cardinality of large datasets with minimal memory usage.
Hypershift: a migration tool and strategy for moving data to $TIMESCALE_DB with minimal downtime.
Hypertable: $TIMESCALE_DB's core abstraction that automatically partitions time-series data for scalability. See Hypertables.
Idempotency: the property where repeated operations produce the same result, crucial for reliable data ingestion and processing.
Ingest rate: the speed at which new data is written to the system, measured in rows per second. Critical for IoT and observability.
Inner product: a mathematical operation that combines two vectors to produce a scalar, used in similarity calculations.
Insert: an SQL operation that adds new rows of data to a database table.
Integer: a data type that represents whole numbers without decimal points.
Intercept: a statistical measure representing the y-intercept in linear regression analysis.
Internet gateway: an AWS VPC component that enables communication between instances in a VPC and the internet.
Interpolation: a method of estimating unknown values that fall between known data points.
IP allow list: a security feature that restricts access to specified IP addresses or ranges.
Isolation level: a database transaction property that defines the degree to which operations in one transaction are isolated from those in other concurrent transactions.
Job: an automated task scheduled to run at specific intervals or triggered by certain conditions.
Job execution: the process of running scheduled background tasks or automated procedures.
JIT (Just-In-Time) compilation: $PG feature that compiles frequently executed query parts for improved performance, available in $TIMESCALE_DB.
Job history: a record of past job executions, including their status, duration, and any errors encountered.
JSON (JavaScript Object Notation): a lightweight data interchange format that is easy for humans to read and write.
JWT (JSON Web Token): a compact, URL-safe means of representing claims to be transferred between two parties.
Latency: the time delay between a request being made and the response being received.
Lifecycle policy: a set of rules that automatically manage data throughout its lifecycle, including retention and deletion.
Live migration: a data migration technique that moves data with minimal or zero downtime.
Load balancer: a service distributing traffic across servers or database nodes to optimize resource use and avoid single points of failure.
Log-Structured Merge (LSM) Tree: a data structure optimized for write-heavy workloads, though $TIMESCALE_DB primarily uses B-tree indexes for balanced read/write performance.
LlamaIndex: a framework for building applications with large language models, providing tools for data ingestion and querying.
LOCF (Last Observation Carried Forward): a method for handling missing data by using the most recent known value.
Logical backup: a backup method that exports data in a human-readable format, allowing for selective restoration.
Logical replication: a $PG feature that replicates data changes at the logical level rather than the physical level.
Logging: the process of recording events, errors, and system activities for monitoring and troubleshooting purposes.
Low: in financial data, the lowest price of a security during a specific time period.
LTTB (Largest-Triangle-Three-Buckets): a downsampling algorithm that preserves the visual characteristics of time-series data.
Manhattan distance: a distance metric calculated as the sum of the absolute differences of their coordinates.
Manual compression: the process of compressing chunks manually rather than through automated policies.
Materialization: the process of computing and storing the results of a query or view for faster access.
Materialized view: a database object that stores the result of a query and can be refreshed periodically.
Memory-optimized query: a query pattern designed to minimize disk I/O by leveraging available RAM and efficient data structures.
Metric: a quantitative measurement used to assess system performance, business outcomes, or operational efficiency.
MFA (Multi-Factor Authentication): a security method that requires two or more verification factors to grant access.
Migration: the process of moving data, applications, or systems from one environment to another. See migration guides.
Monitoring: the continuous observation and measurement of system performance and health.
Multi-tenancy: an architecture pattern supporting multiple customers or applications within a single database instance, with proper isolation.
MQTT (Message Queuing Telemetry Transport): a lightweight messaging protocol designed for small sensors and mobile devices.
MST (Managed Service for TimescaleDB): a fully managed $TIMESCALE_DB service that handles infrastructure and maintenance tasks.
NAT Gateway: a network address translation service that enables instances in a private subnet to connect to the internet.
Node (database node): an individual server within a distributed system, contributing to storage, compute, or replication tasks.
Normalization: database design technique organizing data to reduce redundancy, though time-series data often benefits from denormalized structures.
Not null: a database constraint that ensures a column cannot contain empty values.
Numeric: a $PG data type for storing exact numeric values with user-defined precision.
OAuth: an open standard for access delegation commonly used for token-based authentication and authorization.
Observability: the ability to measure the internal states of a system by examining its outputs.
OLAP (Online Analytical Processing): systems or workloads focused on large-scale, multidimensional, and complex analytical queries.
OLTP (Online Transaction Processing): high-speed transactional systems optimized for data inserts, updates, and short queries.
OHLC: an acronym for Open, High, Low, Close prices, commonly used in financial data analysis.
OHLCV: an extension of OHLC that includes Volume data for complete candlestick analysis.
Open: in financial data, the opening price of a security at the beginning of a trading period.
OpenTelemetry: open standard for collecting, processing, and exporting telemetry data, often stored in time-series databases.
Optimization: the process of making systems, queries, or operations more efficient and performant.
Parallel copy: a technique for copying large amounts of data using multiple concurrent processes to improve performance.
Parallel Query Execution: a $PG feature that uses multiple CPU cores to execute single queries faster, inherited by $TIMESCALE_DB.
Partitioning: the practice of dividing large tables into smaller, more manageable pieces based on certain criteria.
Percentile: a statistical measure that indicates the value below which a certain percentage of observations fall.
Performance: a measure of how efficiently a system operates, often quantified by metrics like throughput, latency, and resource utilization.
pg_basebackup: a $PG utility for taking base backups of a running $PG cluster.
pg_dump: a $PG utility for backing up database objects and data in various formats.
pg_restore: a $PG utility for restoring databases from backup files created by pg_dump.
pgVector: a $PG extension that adds vector similarity search capabilities for AI and machine learning applications. See pgvector.
pgai on $CLOUD_LONG: a cloud solution for building search, RAG, and AI agents with $PG. Enables calling AI embedding and generation models directly from the database using SQL. See pgai.
pgvectorscale: a performance enhancement for pgvector featuring StreamingDiskANN indexing, binary quantization compression, and label-based filtering. See pgvectorscale.
pgvectorizer: a $TIMESCALE_DB tool for automatically vectorizing and indexing data for similarity search.
Physical backup: a backup method that copies the actual database files at the storage level.
PITR (Point-in-Time Recovery): the ability to restore a database to a specific moment in time.
Policy: an automated rule or procedure that performs maintenance tasks like compression, retention, or refresh operations.
Predictive maintenance: the use of time-series data to forecast equipment failure, common in IoT and industrial applications.
Prompt: an input or instruction you give to an Agent. A prompt is normally text, but may be multimodal. For example, images and text.
$PG: an open-source object-relational database system known for its reliability, robustness, and performance.
PostGIS: a $PG extension that adds support for geographic objects and spatial queries.
Primary key: a database constraint that uniquely identifies each row in a table.
psql: an interactive terminal-based front-end to $PG that allows users to type queries interactively.
QPS (Queries Per Second): a measure of database performance indicating how many queries a database can process per second.
Query: a request for data or information from a database, typically written in SQL.
Query performance: a measure of how efficiently database queries execute, including factors like execution time and resource usage.
Query planner/optimizer: a component determining the most efficient strategy for executing SQL queries based on database structure and indexes.
Query planning: the database process of determining the most efficient way to execute a query.
RBAC (Role-Based Access Control): a security model that assigns permissions to users based on their roles within an organization.
Read committed: an isolation level where transactions can read committed changes made by other transactions.
Read scaling: a technique for improving database performance by distributing read queries across multiple database replicas.
Read uncommitted: the lowest isolation level where transactions can read uncommitted changes from other transactions.
Read-only role: a database role with permissions limited to reading data without modification capabilities.
Read replica: a copy of the primary database that serves read-only queries, improving read scalability and geographic distribution.
Real-time analytics: the immediate analysis of incoming data streams, crucial for observability, trading platforms, and IoT monitoring.
Real: a $PG data type for storing single-precision floating-point numbers.
Real-time aggregate: a continuous aggregate that includes both materialized historical data and real-time calculations on recent data.
Refresh policy: an automated rule that determines when and how continuous aggregates are updated with new data.
Region: a geographical area containing multiple data centers, used in cloud computing for data locality and compliance.
Repeatable read: an isolation level that ensures a transaction sees a consistent snapshot of data throughout its execution.
Replica: a copy of a database that can be used for read scaling, backup, or disaster recovery purposes.
Replication: the process of copying and maintaining data across multiple database instances to ensure availability and durability.
Response time: the time it takes for a system to respond to a request, measured from request initiation to response completion.
REST API: a web service architecture that uses HTTP methods to enable communication between applications.
Restore: the process of recovering data from backups to restore a database to a previous state.
Restore point: a snapshot of database state that can be used as a reference point for recovery operations.
Retention policy: an automated rule that determines how long data is kept before being deleted from the system.
Route table: a set of rules that determine where network traffic is directed within a cloud network.
RTO (Recovery Time Objective): the maximum acceptable time that systems can be down after a failure or disaster.
RPO (Recovery Point Objective): the maximum acceptable amount of data loss measured in time after a failure or disaster.
Rowstore: traditional row-oriented data storage where data is stored row by row, optimized for transactional workloads.
SAML (Security Assertion Markup Language): an XML-based standard for exchanging authentication and authorization data between security domains.
Scheduled job: an automated task that runs at predetermined times or intervals.
Schema evolution: the process of modifying database structure over time while maintaining compatibility with existing applications.
Schema: the structure of a database, including tables, columns, relationships, and constraints.
Security group: a virtual firewall that controls inbound and outbound traffic for cloud resources.
Service discovery: mechanisms allowing applications to dynamically locate services like database endpoints, often used in distributed environments.
Segmentwise recompression: a $TIMESCALE_DB compression technique that recompresses data segments to improve compression ratios.
Serializable: the highest isolation level that ensures transactions appear to run serially even when executed concurrently.
Service: see $SERVICE_LONG.
Sharding: horizontal partitioning of data across multiple database instances, distributing load and enabling linear scalability.
SFTP (SSH File Transfer Protocol): a secure version of FTP that encrypts both commands and data during transmission.
SkipScan: query optimization for DISTINCT operations that incrementally jumps between ordered values without reading intermediate rows. Uses a Custom Scan node to efficiently traverse ordered indexes, dramatically improving performance over traditional DISTINCT queries.
Similarity search: a technique for finding items that are similar to a given query item, often used with vector embeddings.
SLA (Service Level Agreement): a contract that defines the expected level of service between a provider and customer.
SLI (Service Level Indicator): a quantitative measure of some aspect of service quality.
SLO (Service Level Objective): a target value or range for service quality measured by an SLI.
Slope: a statistical measure representing the rate of change in linear regression analysis.
SMTP (Simple Mail Transfer Protocol): an internet standard for email transmission across networks.
Snapshot: a point-in-time copy of data that can be used for backup and recovery purposes.
SP-GiST (Space-Partitioned Generalized Search Tree): a $PG index type for data structures that naturally partition search spaces.
Storage optimization: techniques for reducing storage costs and improving performance through compression, tiering, and efficient data organization.
Streaming data: continuous flows of data generated by devices, logs, or sensors, requiring high-ingest, real-time storage solutions.
SQL (Structured Query Language): a programming language designed for managing and querying relational databases.
SSH (Secure Shell): a cryptographic network protocol for secure communication over an unsecured network.
SSL (Secure Sockets Layer): a security protocol that establishes encrypted links between networked computers.
Standard $SERVICE_SHORT: a regular $SERVICE_LONG that includes the resources and features according to the pricing plan. You can create standard $SERVICE_SHORTs under any of the paid plans.
Streaming replication: a $PG replication method that continuously sends write-ahead log records to standby servers.
Synthetic monitoring: simulated transactions or probes used to test system health, generating time-series metrics for performance analysis.
Table: a database object that stores data in rows and columns, similar to a spreadsheet.
Tablespace: a $PG storage structure that defines where database objects are physically stored on disk.
TCP (Transmission Control Protocol): a connection-oriented protocol that ensures reliable data transmission between applications.
TDigest: a probabilistic data structure for accurate estimation of percentiles in distributed systems.
Telemetry: the collection of real-time data from systems or devices for monitoring and analysis.
Text: a $PG data type for storing variable-length character strings.
Throughput: a measure of system performance indicating the amount of work performed or data processed per unit of time.
Tiered storage: a storage strategy that automatically moves data between different storage classes based on access patterns and age.
$CLOUD_LONG: $COMPANY's managed cloud platform that provides $TIMESCALE_DB as a fully managed solution with additional features.
Tiger Lake: $COMPANY's service for integrating operational databases with data lake architectures.
$SERVICE_LONG: an instance of optimized $PG extended with database engine innovations such as $TIMESCALE_DB, in a cloud infrastructure that delivers speed without sacrifice. You can create free $SERVICE_SHORTs and standard $SERVICE_SHORTs.
Time series: data points indexed and ordered by time, typically representing how values change over time.
Time-weighted average: a statistical calculation that gives more weight to values based on the duration they were held.
Time bucketing: grouping timestamps into uniform intervals for analysis, commonly used with hyperfunctions.
Time-series forecasting: the application of statistical models to time-series data to predict future trends or events.
$TIMESCALE_DB: an open-source $PG extension for real-time analytics that provides scalability and performance optimizations.
Timestamp: a data type that stores date and time information without timezone data.
Timestamptz: a $PG data type that stores timestamp with timezone information.
TLS (Transport Layer Security): a cryptographic protocol that provides security for communication over networks.
Tombstone: marker indicating deleted data in append-only systems, requiring periodic cleanup processes.
Transaction isolation: the database property controlling the visibility of uncommitted changes between concurrent transactions.
TPS (Transactions Per Second): a measure of database performance indicating transaction processing capacity.
Transaction: a unit of work performed against a database that must be completed entirely or not at all.
Trigger: a database procedure that automatically executes in response to certain events on a table or view.
UDP (User Datagram Protocol): a connectionless communication protocol that provides fast but unreliable data transmission.
Unique: a database constraint that ensures all values in a column or combination of columns are distinct.
Uptime: the amount of time that a system has been operational and available for use.
Usage-based storage: a billing model where storage costs are based on actual data stored rather than provisioned capacity.
UUID (Universally Unique Identifier): a 128-bit identifier used to uniquely identify information without central coordination.
Vacuum: a $PG maintenance operation that reclaims storage and updates database statistics.
Varchar: a variable-length character data type that can store strings up to a specified maximum length.
Vector operations: SIMD (Single Instruction, Multiple Data) optimizations for processing arrays of data, improving analytical query performance.
Vertical scaling (scale up): increasing system capacity by adding more power (CPU, RAM) to existing machines, as opposed to horizontal scaling.
Visualization tool: a platform or dashboard used to display time-series data in charts, graphs, and alerts for easier monitoring and analysis.
Vector: a mathematical object with magnitude and direction, used in machine learning for representing data as numerical arrays.
VPC (Virtual Private Cloud): a virtual network dedicated to your cloud account that provides network isolation.
VWAP (Volume Weighted Average Price): a financial indicator that shows the average price weighted by volume over a specific time period.
WAL (Write-Ahead Log): $PG's method for ensuring data integrity by writing changes to a log before applying them to data files.
Warm storage: a storage tier that balances access speed and cost, suitable for data accessed occasionally.
Watermark: a timestamp that tracks the progress of continuous aggregate materialization.
WebSocket: a communication protocol that provides full-duplex communication channels over a single TCP connection.
Window function: an SQL function that performs calculations across related rows, particularly useful for time-series analytics and trend analysis.
Workload management: techniques for prioritizing and scheduling different types of database operations to optimize overall system performance.
XML (eXtensible Markup Language): a markup language that defines rules for encoding documents in a format that is both human-readable and machine-readable.
YAML (YAML Ain't Markup Language): a human-readable data serialization standard commonly used for configuration files.
Zero downtime: a system design goal where services remain available during maintenance, upgrades, or migrations without interruption.
Zero-downtime migration: migration strategies that maintain service availability throughout the transition process, often using techniques like dual-write and gradual cutover.