InfluxDB and Time Series Databases Cheat Sheet

Back to DatabasesUpdated 2026-05-15

Time series databases (TSDBs) are specialized data stores optimized for timestamped data — sensor readings, server metrics, financial ticks, application logs — where every record is indexed by time and queries typically involve time ranges and aggregations. InfluxDB is a leading TSDB built on Apache Arrow and Parquet (v3.0), offering high ingestion throughput, columnar storage, and multiple query languages (SQL, InfluxQL, Flux). Unlike general-purpose databases, TSDBs prioritize write performance over update/delete operations, manage data lifecycle through automatic downsampling and retention policies, and handle high-cardinality tags with specialized indexing. Understanding the data model (measurements, tags, fields, timestamps) is key — tags are indexed metadata for filtering, fields hold measured values, and proper schema design directly impacts cardinality and query performance.

What This Cheat Sheet Covers

This topic spans 15 focused tables and 142 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: InfluxDB Line Protocol ElementsTable 2: InfluxDB 3.0 Storage Architecture (FDAP Stack)Table 3: Flux Query Language Core FunctionsTable 4: InfluxDB Data Types and Type ConstraintsTable 5: Buckets, Retention Policies, and Data LifecycleTable 6: Continuous Aggregation and DownsamplingTable 7: Telegraf Data Collection AgentTable 8: Time-Based Aggregation and Selector FunctionsTable 9: Tag Cardinality Management Best PracticesTable 10: Time Series Database Comparison (2026)Table 11: Monitoring and Observability Use CasesTable 12: InfluxQL vs Flux vs SQL Query LanguagesTable 13: Write API, Batching, and Performance OptimizationTable 14: Query Performance and Optimization TechniquesTable 15: Client Libraries and Integration

Table 1: InfluxDB Line Protocol Elements

Element	Example	Description
Measurement	`temperature,location=us-west`	Table name in the data model; groups related metrics; required first element in line protocol; comma-separated from tagset with no space.
Tag set	`location=us-west,sensor=A`	Indexed metadata for filtering and grouping; comma-separated key-value pairs; always stored as strings; create series (unique combinations); directly impact cardinality.
Field set	`temp=72.5,humidity=45i`	Measured values (floats, integers, strings, booleans); space-separated from tagset; at least one field required; not indexed; use suffixes `i` (integer), `u` (unsigned).
Timestamp	1672531200000000000	Unix nanosecond precision by default; optional (server time if omitted); space-separated from fields; InfluxDB stores and returns all timestamps in UTC.
Line protocol syntax	`weather,city=LA temp=85.3 1672531200000000000`	Format: `measurement[,tag=val]* field=val[,field=val]* [timestamp]` Each line represents one data point; newline-separated; whitespace between tagset/fieldset/timestamp is significant.

Table 1: InfluxDB Line Protocol Elements

Element	Example	Description
Measurement	`temperature,location=us-west`	Table name in the data model; groups related metrics; required first element in line protocol; comma-separated from tagset with no space.
Tag set	`location=us-west,sensor=A`	Indexed metadata for filtering and grouping; comma-separated key-value pairs; always stored as strings; create series (unique combinations); directly impact cardinality.
Field set	`temp=72.5,humidity=45i`	Measured values (floats, integers, strings, booleans); space-separated from tagset; at least one field required; not indexed; use suffixes `i` (integer), `u` (unsigned).
Timestamp	1672531200000000000	Unix nanosecond precision by default; optional (server time if omitted); space-separated from fields; InfluxDB stores and returns all timestamps in UTC.
Line protocol syntax	`weather,city=LA temp=85.3 1672531200000000000`	Format: `measurement[,tag=val]* field=val[,field=val]* [timestamp]` Each line represents one data point; newline-separated; whitespace between tagset/fieldset/timestamp is significant.