## Get started with {{% product-name %}} InfluxDB is a database built to collect, process, transform, and store event and time series data, and is ideal for use cases that require real-time ingest and fast query response times to build user interfaces, monitoring, and automation solutions. Common use cases include: - Monitoring sensor data - Server monitoring - Application performance monitoring - Network monitoring - Financial market and trading analytics - Behavioral analytics InfluxDB is optimized for scenarios where near real-time data monitoring is essential and queries need to return quickly to support user experiences such as dashboards and interactive user interfaces. {{% product-name %}} is built on InfluxDB 3 Core, the InfluxDB 3 open source release. Core's feature highlights include: * Diskless architecture with object storage support (or local disk with no dependencies) * Fast query response times (under 10ms for last-value queries, or 30ms for distinct metadata) * Embedded Python VM for plugins and triggers * Parquet file persistence * Compatibility with InfluxDB 1.x and 2.x write APIs The Enterprise version adds the following features to Core: * Historical query capability and single series indexing * High availability * Read replicas * Enhanced security (coming soon) * Row-level delete support (coming soon) * Integrated admin UI (coming soon) ### What's in this guide This guide covers Enterprise as well as InfluxDB 3 Core, including the following topics: * [Install and startup](#install-and-startup) * [Data Model](#data-model) * [Write data to the database](#write-data) * [Query the database](#query-the-database) * [Last values cache](#last-values-cache) * [Distinct values cache](#distinct-values-cache) * [Python plugins and the processing engine](#python-plugins-and-the-processing-engine) * [Multi-server setups](#multi-server-setup) ### Install and startup {{% product-name %}} runs on **Linux**, **macOS**, and **Windows**. {{% tabs-wrapper %}} {{% tabs %}} [Linux or macOS](#linux-or-macos) [Windows](#windows) [Docker](#docker) {{% /tabs %}} {{% tab-content %}} To get started quickly, download and run the install script--for example, using [curl](https://curl.se/download.html): ```bash curl -O https://www.influxdata.com/d/install_influxdb3.sh \ && sh install_influxdb3.sh enterprise ``` Or, download and install [build artifacts](/influxdb3/enterprise/install/#download-influxdb-3-enterprise-binaries): - [Linux | x86_64 | GNU](https://dl.influxdata.com/influxdb/snapshots/influxdb3-enterprise_x86_64-unknown-linux-gnu.tar.gz) • [sha256](https://dl.influxdata.com/influxdb/snapshots/influxdb3-enterprise_x86_64-unknown-linux-gnu.tar.gz.sha256) - [Linux | ARM64 | GNU](https://dl.influxdata.com/influxdb/snapshots/influxdb3-enterprise_aarch64-unknown-linux-gnu.tar.gz) • [sha256](https://dl.influxdata.com/influxdb/snapshots/influxdb3-enterprise_aarch64-unknown-linux-gnu.tar.gz.sha256) - [macOS | ARM64](https://dl.influxdata.com/influxdb/snapshots/influxdb3-enterprise_aarch64-apple-darwin.tar.gz) • [sha256](https://dl.influxdata.com/influxdb/snapshots/influxdb3-enterprise_aarch64-apple-darwin.tar.gz.sha256) > [!Note] > macOS Intel builds are coming soon. {{% /tab-content %}} {{% tab-content %}} Download and install the {{% product-name %}} [Windows (x86) binary](https://dl.influxdata.com/influxdb/snapshots/influxdb3-enterprise_x86_64-pc-windows-gnu.tar.gz) • [sha256](https://dl.influxdata.com/influxdb/snapshots/influxdb3-enterprise_x86_64-pc-windows-gnu.tar.gz.sha256) {{% /tab-content %}} {{% tab-content %}} The [`influxdb3-enterprise` image](https://quay.io/repository/influxdb/influxdb3-enterprise?tab=tags&tag=latest) is available for x86_64 (AMD64) and ARM64 architectures. Pull the image: ```bash docker pull quay.io/influxdb/influxdb3-enterprise:latest ``` {{% /tab-content %}} {{% /tabs-wrapper %}} _Build artifacts and images update with every merge into the {{% product-name %}} `main` branch._ #### Verify the install After you have installed {{% product-name %}}, enter the following command to verify that it completed successfully: ```bash influxdb3 --version ``` If your system doesn't locate `influxdb3`, then `source` the configuration file (for example, .bashrc, .zshrc) for your shell--for example: ```zsh source ~/.zshrc ``` #### Start InfluxDB To start your InfluxDB instance, use the `influxdb3 serve` command and provide the following: - `--object-store`: Specifies the type of Object store to use. InfluxDB supports the following: local file system (`file`), `memory`, S3 (and compatible services like Ceph or Minio) (`s3`), Google Cloud Storage (`google`), and Azure Blob Storage (`azure`). - `--node-id`: A string identifier that determines the server's storage path within the configured storage location, and, in a multi-node setup, is used to reference the node. > [!Note] > #### Diskless architecture > > InfluxDB 3 supports a diskless architecture that can operate with object > storage alone, eliminating the need for locally attached disks. > {{% product-name %}} can also work with only local disk storage when needed. The following examples show how to start InfluxDB 3 with different object store configurations: ```bash # Memory object store # Stores data in RAM; doesn't persist data influxdb3 serve \ --node-id host01 \ --cluster-id cluster01 \ --object-store memory ``` ```bash # Filesystem object store # Provide the filesystem directory influxdb3 serve \ --node-id host01 \ --cluster-id cluster01 \ --object-store file \ --data-dir ~/.influxdb3 ``` To run the [Docker image](/influxdb3/enterprise/install/#docker-image) and persist data to the filesystem, mount a volume for the Object store-for example, pass the following options: - `-v /path/on/host:/path/in/container`: Mounts a directory from your filesystem to the container - `--object-store file --data-dir /path/in/container`: Uses the mount for server storage > [!Note] > > The {{% product-name %}} Docker image exposes port `8181`, the `influxdb3` server default for HTTP connections. > To map the exposed port to a different port when running a container, see the Docker guide for [Publishing and exposing ports](https://docs.docker.com/get-started/docker-concepts/running-containers/publishing-ports/). ```bash # Filesystem object store with Docker # Create a mount # Provide the mount path docker run -it \ -v /path/on/host:/path/in/container \ quay.io/influxdb/influxdb3-enterprise:latest serve \ --node-id my_host \ --cluster-id my_cluster \ --object-store file \ --data-dir /path/in/container ``` ```bash # S3 object store (default is the us-east-1 region) # Specify the Object store type and associated options influxdb3 serve \ --node-id host01 \ --cluster-id cluster01 \ --object-store s3 \ --bucket BUCKET \ --aws-access-key-id AWS_ACCESS_KEY_ID \ --aws-secret-access-key AWS_SECRET_ACCESS_KEY ``` ```bash # Minio or other open source object store # (using the AWS S3 API with additional parameters) # Specify the object store type and associated options influxdb3 serve \ host01 \ --cluster-id cluster01 \ --object-store s3 \ --bucket BUCKET \ --aws-access-key-id AWS_ACCESS_KEY_ID \ --aws-secret-access-key AWS_SECRET_ACCESS_KEY \ --aws-endpoint ENDPOINT \ --aws-allow-http ``` For more information about server options, use the CLI help: ```bash influxdb3 serve --help ``` > [!Important] > #### Stopping the Docker container > > Currently, a bug prevents using `Ctrl-c` to stop an InfluxDB 3 container. > Use the `docker kill` command to stop the container: > > 1. Enter the following command to find the container ID: > > ```bash > docker ps -a > ``` > 2. Enter the command to stop the container: > > ```bash > docker kill > ``` #### Licensing When starting {{% product-name %}} for the first time, it prompts you to enter an email address for verification. You will receive an email with a verification link. Upon verification, the license creation, retrieval, and application are automated. _During the alpha period, licenses are valid until May 7, 2025._ ### Data model The database server contains logical databases, which have tables, which have columns. Compared to previous versions of InfluxDB you can think of a database as a `bucket` in v2 or as a `db/retention_policy` in v1. A `table` is equivalent to a `measurement`, which has columns that can be of type `tag` (a string dictionary), `int64`, `float64`, `uint64`, `bool`, or `string` and finally every table has a `time` column that is a nanosecond precision timestamp. In InfluxDB 3, every table has a primary key--the ordered set of tags and the time--for its data. This is the sort order used for all Parquet files that get created. When you create a table, either through an explicit call or by writing data into a table for the first time, it sets the primary key to the tags in the order they arrived. This is immutable. Although InfluxDB is still a _schema-on-write_ database, the tag column definitions for a table are immutable. Tags should hold unique identifying information like `sensor_id`, or `building_id` or `trace_id`. All other data should be kept in fields. You will be able to add fast last N value and distinct value lookups later for any column, whether it is a field or a tag. ### Write data InfluxDB is a schema-on-write database. You can start writing data and InfluxDB creates the logical database, tables, and their schemas on the fly. After a schema is created, InfluxDB validates future write requests against it before accepting the data. Subsequent requests can add new fields on-the-fly, but can't add new tags. {{% product-name %}} provides three write API endpoints that respond to HTTP `POST` requests: #### /api/v3/write_lp endpoint {{% product-name %}} adds the `/api/v3/write_lp` endpoint. {{}} This endpoint accepts the same line protocol syntax as previous versions, and supports the `?accept_partial=` parameter, which lets you accept or reject partial writes (default is `true`). #### /api/v2/write InfluxDB v2 compatibility endpoint Provides backwards compatibility with clients that can write data to InfluxDB OSS v2.x and Cloud 2 (TSM). {{}} #### /write InfluxDB v1 compatibility endpoint Provides backwards compatibility for clients that can write data to InfluxDB v1.x {{}} Keep in mind that these compatibility APIs differ from the v1 and v2 APIs in previous versions in the following ways: - Tags in a table (measurement) are _immutable_ - A tag and a field can't have the same name within a table. #### Write line protocol The following code block is an example of time series data in [line protocol](/influxdb3/core/reference/syntax/line-protocol/) syntax: - `cpu`: the table name. - `host`, `region`, `applications`: the tags. A tag set is an ordered, comma-separated list of key/value pairs where the values are strings. - `val`, `usage_percent`, `status`: the fields. A field set is a comma-separated list of key/value pairs. - timestamp: If you don't specify a timestamp, InfluxData uses the time when data is written. The default precision is a nanosecond epoch. To specify a different precision, pass the `precision` query parameter. ``` cpu,host=Alpha,region=us-west,application=webserver val=1i,usage_percent=20.5,status="OK" cpu,host=Bravo,region=us-east,application=database val=2i,usage_percent=55.2,status="OK" cpu,host=Charlie,region=us-west,application=cache val=3i,usage_percent=65.4,status="OK" cpu,host=Bravo,region=us-east,application=database val=4i,usage_percent=70.1,status="Warn" cpu,host=Bravo,region=us-central,application=database val=5i,usage_percent=80.5,status="OK" cpu,host=Alpha,region=us-west,application=webserver val=6i,usage_percent=25.3,status="Warn" ``` ##### Example: write data using the influxdb3 CLI If you save the preceding line protocol to a file (for example, `server_data`), then you can use the `influxdb3` CLI to write the data--for example: ```bash influxdb3 write --database mydb --file server_data ``` ##### Example: write data using the /api/v3 HTTP API The following examples show how to write data using `curl` and the `/api/3/write_lp` HTTP endpoint. To show the difference between accepting and rejecting partial writes, line `2` in the example contains a `string` value for a `float` field (`temp=hi`). ###### Partial write of line protocol occurred With `accept_partial=true`: ``` * upload completely sent off: 59 bytes < HTTP/1.1 400 Bad Request < transfer-encoding: chunked < date: Wed, 15 Jan 2025 19:35:36 GMT < * Connection #0 to host localhost left intact { "error": "partial write of line protocol occurred", "data": [ { "original_line": "dquote> home,room=Sunroom temp=hi", "line_number": 2, "error_message": "No fields were provided" } ] } ``` Line `1` is written and queryable. The response is an HTTP error (`400`) status, and the response body contains the error message `partial write of line protocol occurred` with details about the problem line. ###### Parsing failed for write_lp endpoint With `accept_partial=false`: ```bash curl -v "http://{{< influxdb/host >}}/api/v3/write_lp?db=sensors&precision=auto&accept_partial=false" \ --data-raw "home,room=Sunroom temp=96 home,room=Sunroom temp=hi" ``` The response is the following: ``` < HTTP/1.1 400 Bad Request < transfer-encoding: chunked < date: Wed, 15 Jan 2025 19:28:27 GMT < * Connection #0 to host localhost left intact { "error": "parsing failed for write_lp endpoint", "data": { "original_line": "home,room=Sunroom temp=hi", "line_number": 2, "error_message": "No fields were provided" } } ``` InfluxDB rejects all points in the batch. The response is an HTTP error (`400`) status, and the response body contains `parsing failed for write_lp endpoint` and details about the problem line. ### Data flow The figure below shows how written data flows through the database. {{< img-hd src="/img/influxdb/influxdb-3-write-path.png" alt="Write Path for InfluxDB 3 Core & Enterprise" />}} 1. **Incoming writes**: The system validates incoming data and stores it in the write buffer (in memory). If [`no_sync=true`](#no-sync-write-option), the server sends a response to acknowledge the write. 2. **WAL flush**: Every second (default), the system flushes the write buffer to the Write-Ahead Log (WAL) for persistence in the Object store. If [`no_sync=false`](#no-sync-write-option) (default), the server sends a response to acknowledge the write. 3. **Query availability**: After WAL persistence completes, data moves to the queryable buffer where it becomes available for queries. By default, the server keeps up to 900 WAL files (15 minutes of data) buffered. 4. **Long-term storage in Parquet**: Every ten minutes (default), the system persists the oldest data from the queryable buffer to the Object store in Parquet format. InfluxDB keeps the remaining data (the most recent 5 minutes) in memory. 5. **In-memory cache**: InfluxDB puts Parquet files into an in-memory cache so that queries against the most recently persisted data don't have to go to object storage. #### Write responses By default, InfluxDB acknowledges writes after flushing the WAL file to the Object store (occurring every second). For high write throughput, you can send multiple concurrent write requests. #### Use no_sync for immediate write responses To reduce the latency of writes, use the `no_sync` write option, which acknowledges writes _before_ WAL persistence completes. When `no_sync=true`, InfluxDB validates the data, writes the data to the WAL, and then immediately responds to the client, without waiting for persistence to the Object store. Using `no_sync=true` is best when prioritizing high-throughput writes over absolute durability. - Default behavior (`no_sync=false`): Waits for data to be written to the Object store before acknowledging the write. Reduces the risk of data loss, but increases the latency of the response. - With `no_sync=true`: Reduces write latency, but increases the risk of data loss in case of a crash before WAL persistence. ##### Immediate write using the HTTP API The `no_sync` parameter controls when writes are acknowledged--for example: ```bash curl "http://localhost:8181/api/v3/write_lp?db=sensors&precision=auto&no_sync=true" \ --data-raw "home,room=Sunroom temp=96" ``` ##### Immediate write using the influxdb3 CLI The `no_sync` CLI option controls when writes are acknowledged--for example: ```bash influxdb3 write \ --bucket mydb \ --org my_org \ --token my-token \ --no-sync ``` ### Create a database or table To create a database without writing data, use the `create` subcommand--for example: ```bash influxdb3 create database mydb ``` To learn more about a subcommand, use the `-h, --help` flag: ```bash influxdb3 create -h ``` ### Query data InfluxDB 3 now supports native SQL for querying, in addition to InfluxQL, an SQL-like language customized for time series queries. > [!Note] > Flux, the language introduced in InfluxDB 2.0, is **not** supported in InfluxDB 3. The quickest way to get started querying is to use the `influxdb3` CLI (which uses the Flight SQL API over HTTP2). The `query` subcommand includes options to help ensure that the right database is queried with the correct permissions. Only the `--database` option is required, but depending on your specific setup, you may need to pass other options, such as host, port, and token. | Option | Description | Required | |---------|-------------|--------------| | `--host` | The host URL of the server [default: `http://127.0.0.1:8181`] to query | No | | `--database` | The name of the database to operate on | Yes | | `--token` | The authentication token for the {{% product-name %}} server | No | | `--language` | The query language of the provided query string [default: `sql`] [possible values: `sql`, `influxql`] | No | | `--format` | The format in which to output the query [default: `pretty`] [possible values: `pretty`, `json`, `jsonl`, `csv`, `parquet`] | No | | `--output` | The path to output data to | No | #### Example: query `“SHOW TABLES”` on the `servers` database: ```console $ influxdb3 query --database servers "SHOW TABLES" +---------------+--------------------+--------------+------------+ | table_catalog | table_schema | table_name | table_type | +---------------+--------------------+--------------+------------+ | public | iox | cpu | BASE TABLE | | public | information_schema | tables | VIEW | | public | information_schema | views | VIEW | | public | information_schema | columns | VIEW | | public | information_schema | df_settings | VIEW | | public | information_schema | schemata | VIEW | +---------------+--------------------+--------------+------------+ ``` #### Example: query the `cpu` table, limiting to 10 rows: ```console $ influxdb3 query --database servers "SELECT DISTINCT usage_percent, time FROM cpu LIMIT 10" +---------------+---------------------+ | usage_percent | time | +---------------+---------------------+ | 63.4 | 2024-02-21T19:25:00 | | 25.3 | 2024-02-21T19:06:40 | | 26.5 | 2024-02-21T19:31:40 | | 70.1 | 2024-02-21T19:03:20 | | 83.7 | 2024-02-21T19:30:00 | | 55.2 | 2024-02-21T19:00:00 | | 80.5 | 2024-02-21T19:05:00 | | 60.2 | 2024-02-21T19:33:20 | | 20.5 | 2024-02-21T18:58:20 | | 85.2 | 2024-02-21T19:28:20 | +---------------+---------------------+ ``` ### Query using the CLI for InfluxQL [InfluxQL](/influxdb3/version/reference/influxql/) is an SQL-like language developed by InfluxData with specific features tailored for leveraging and working with InfluxDB. It’s compatible with all versions of InfluxDB, making it a good choice for interoperability across different InfluxDB installations. To query using InfluxQL, enter the `influxdb3 query` subcommand and specify `influxql` in the language option--for example: ```bash influxdb3 query \ --database servers \ --language influxql \ "SELECT DISTINCT usage_percent FROM cpu WHERE time >= now() - 1d" ``` ### Query using the API InfluxDB 3 supports Flight (gRPC) APIs and an HTTP API. To query your database using the HTTP API, send a request to the `/api/v3/query_sql` or `/api/v3/query_influxql` endpoints. In the request, specify the database name in the `db` parameter and a query in the `q` parameter. You can pass parameters in the query string or inside a JSON object. Use the `format` parameter to specify the response format: `pretty`, `jsonl`, `parquet`, `csv`, and `json`. Default is `json`. ##### Example: Query passing URL-encoded parameters The following example sends an HTTP `GET` request with a URL-encoded SQL query: ```bash curl -v "http://{{< influxdb/host >}}/api/v3/query_sql?db=servers&q=select+*+from+cpu+limit+5" ``` ##### Example: Query passing JSON parameters The following example sends an HTTP `POST` request with parameters in a JSON payload: ```bash curl http://{{< influxdb/host >}}/api/v3/query_sql \ --data '{"db": "server", "q": "select * from cpu limit 5"}' ``` ### Query using the Python client Use the InfluxDB 3 Python library to interact with the database and integrate with your application. We recommend installing the required packages in a Python virtual environment for your specific project. To get started, install the `influxdb3-python` package. ```bash pip install influxdb3-python ``` From here, you can connect to your database with the client library using just the **host** and **database name: ```python from influxdb_client_3 import InfluxDBClient3 client = InfluxDBClient3( host='http://{{< influxdb/host >}}', database='servers' ) ``` The following example shows how to query using SQL, and then use PyArrow to explore the schema and process results: ```python from influxdb_client_3 import InfluxDBClient3 client = InfluxDBClient3( host='http://{{< influxdb/host >}}', database='servers' ) # Execute the query and return an Arrow table table = client.query( query="SELECT * FROM cpu LIMIT 10", language="sql" ) print("\n#### View Schema information\n") print(table.schema) print("\n#### Use PyArrow to read the specified columns\n") print(table.column('usage_active')) print(table.select(['host', 'usage_active'])) print(table.select(['time', 'host', 'usage_active'])) print("\n#### Use PyArrow compute functions to aggregate data\n") print(table.group_by('host').aggregate([])) print(table.group_by('cpu').aggregate([('time_system', 'mean')])) ``` For more information about the Python client library, see the [`influxdb3-python` repository](https://github.com/InfluxCommunity/influxdb3-python) in GitHub. ### Last values cache {{% product-name %}} supports a **last-n values cache** which stores the last N values in a series or column hierarchy in memory. This gives the database the ability to answer these kinds of queries in under 10 milliseconds. You can use the `influxdb3` CLI to create a last value cache. ```bash influxdb3 create last_cache \ -d \ -t \ [CACHE_NAME] ``` Consider the following `cpu` sample table: | host | application | time | usage\_percent | status | | ----- | ----- | ----- | ----- | ----- | | Bravo | database | 2024-12-11T10:00:00 | 55.2 | OK | | Charlie | cache | 2024-12-11T10:00:00 | 65.4 | OK | | Bravo | database | 2024-12-11T10:01:00 | 70.1 | Warn | | Bravo | database | 2024-12-11T10:01:00 | 80.5 | OK | | Alpha | webserver | 2024-12-11T10:02:00 | 25.3 | Warn | The following command creates a last value cache named `cpuCache`: ```bash influxdb3 create last_cache \ --database servers \ --table cpu \ --key-columns host,application \ --value-columns usage_percent,status \ --count 5 cpuCache ``` _You can create a last values cache per time series, but be mindful of high cardinality tables that could take excessive memory._ #### Query a last values cache To use the LVC, call it using the `last_cache()` function in your query--for example: ```bash influxdb3 query \ --database servers \ "SELECT * FROM last_cache('cpu', 'cpuCache') WHERE host = 'Bravo';" ``` > [!Note] > #### Only works with SQL > > The Last values cache only works with SQL, not InfluxQL; SQL is the default language. #### Delete a Last values cache Use the `influxdb3` CLI to [delete a last values cache](/influxdb3/version/reference/cli/influxdb3/delete/last_cache/) ```bash influxdb3 delete last_cache \ --database \ --table
\ --cache-name ``` ### Distinct values cache Similar to the Last values cache, the database can cache in RAM the distinct values for a single column in a table or a hierarchy of columns. This is useful for fast metadata lookups, which can return in under 30 milliseconds. Many of the options are similar to the last value cache. You can use the `influxdb3` CLI to [create a distinct values cache](/influxdb3/version/reference/cli/influxdb3/create/distinct_cache/). ```bash influxdb3 create distinct_cache \ --database \ --table
\ --columns \ [CACHE_NAME] ``` Consider the following `cpu` sample table: | host | application | time | usage\_percent | status | | ----- | ----- | ----- | ----- | ----- | | Bravo | database | 2024-12-11T10:00:00 | 55.2 | OK | | Charlie | cache | 2024-12-11T10:00:00 | 65.4 | OK | | Bravo | database | 2024-12-11T10:01:00 | 70.1 | Warn | | Bravo | database | 2024-12-11T10:01:00 | 80.5 | OK | | Alpha | webserver | 2024-12-11T10:02:00 | 25.3 | Warn | The following command creates a distinct values cache named `cpuDistinctCache`: ```bash influxdb3 create distinct_cache \ --database servers \ --table cpu \ --columns host,application \ cpuDistinctCache ``` #### Query a distinct values cache To use the distinct values cache, call it using the `distinct_cache()` function in your query--for example: ```bash influxdb3 query \ --database servers \ "SELECT * FROM distinct_cache('cpu', 'cpuDistinctCache')" ``` > [!Note] > #### Only works with SQL > > The distinct cache only works with SQL, not InfluxQL; SQL is the default language. #### Delete a distinct values cache Use the `influxdb3` CLI to [delete a distinct values cache](/influxdb3/version/reference/cli/influxdb3/delete/distinct_cache/) ```bash influxdb3 delete distinct_cache \ --database \ --table
\ --cache-name ``` ### Python plugins and the Processing engine The InfluxDB 3 Processing engine is an embedded Python VM for running code inside the database to process and transform data. To activate the Processing engine, pass the `--plugin-dir ` option when starting the {{% product-name %}} server. `PLUGIN_DIR` is your filesystem location for storing [plugin](#plugin) files for the Processing engine to run. #### Plugin A plugin is a Python function that has a signature compatible with a Processing engine [trigger](#trigger). #### Trigger When you create a trigger, you specify a [plugin](#plugin), a database, optional arguments, and a _trigger-spec_, which defines when the plugin is executed and what data it receives. ##### Trigger types InfluxDB 3 provides the following types of triggers, each with specific trigger-specs: - **On WAL flush**: Sends a batch of written data (for a specific table or all tables) to a plugin (by default, every second). - **On Schedule**: Executes a plugin on a user-configured schedule (using a crontab or a duration); useful for data collection and deadman monitoring. - **On Request**: Binds a plugin to a custom HTTP API endpoint at `/api/v3/engine/`. The plugin receives the HTTP request headers and content, and can then parse, process, and send the data into the database or to third-party services. ### Test, create, and trigger plugin code ##### Example: Python plugin for WAL rows ```python # This is the basic structure for Python plugin code that runs in the # InfluxDB 3 Processing engine. # When creating a trigger, you can provide runtime arguments to your plugin, # allowing you to write generic code that uses variables such as monitoring thresholds, environment variables, and host names. # # Use the following exact signature to define a function for the WAL flush # trigger. # When you create a trigger for a WAL flush plugin, you specify the database # and tables that the plugin receives written data from on every WAL flush # (default is once per second). def process_writes(influxdb3_local, table_batches, args=None): # here you can see logging. for now this won't do anything, but soon # we'll capture this so you can query it from system tables if args and "arg1" in args: influxdb3_local.info("arg1: " + args["arg1"]) # here we're using arguments provided at the time the trigger was set up # to feed into paramters that we'll put into a query query_params = {"host": "foo"} # here's an example of executing a parameterized query. Only SQL is supported. # It will query the database that the trigger is attached to by default. We'll # soon have support for querying other DBs. query_result = influxdb3_local.query("SELECT * FROM cpu where host = '$host'", query_params) # the result is a list of Dict that have the column name as key and value as # value. If you run the WAL test plugin with your plugin against a DB that # you've written data into, you'll be able to see some results influxdb3_local.info("query result: " + str(query_result)) # this is the data that is sent when the WAL is flushed of writes the server # received for the DB or table of interest. One batch for each table (will # only be one if triggered on a single table) for table_batch in table_batches: # here you can see that the table_name is available. influxdb3_local.info("table: " + table_batch["table_name"]) # example to skip the table we're later writing data into if table_batch["table_name"] == "some_table": continue # and then the individual rows, which are Dict with keys of the column names and values for row in table_batch["rows"]: influxdb3_local.info("row: " + str(row)) # this shows building a line of LP to write back to the database. tags must go first and # their order is important and must always be the same for each individual table. Then # fields and lastly an optional time, which you can see in the next example below line = LineBuilder("some_table")\ .tag("tag1", "tag1_value")\ .tag("tag2", "tag2_value")\ .int64_field("field1", 1)\ .float64_field("field2", 2.0)\ .string_field("field3", "number three") # this writes it back (it actually just buffers it until the completion of this function # at which point it will write everything back that you put in) influxdb3_local.write(line) # here's another example, but with us setting a nanosecond timestamp at the end other_line = LineBuilder("other_table") other_line.int64_field("other_field", 1) other_line.float64_field("other_field2", 3.14) other_line.time_ns(1302) # and you can see that we can write to any DB in the server influxdb3_local.write_to_db("mytestdb", other_line) # just some log output as an example influxdb3_local.info("done") ``` ##### Test a plugin on the server Test your InfluxDB 3 plugin safely without affecting written data. During a plugin test: - A query executed by the plugin queries against the server you send the request to. - Writes aren't sent to the server but are returned to you. To test a plugin, do the following: 1. Create a _plugin directory_--for example, `/path/to/.influxdb/plugins` 2. [Start the InfluxDB server](#start-influxdb) and include the `--plugin-dir ` option. 3. Save the [example plugin code](#example-python-plugin-for-wal-flush) to a plugin file inside of the plugin directory. If you haven't yet written data to the table in the example, comment out the lines where it queries. 4. To run the test, enter the following command with the following options: - `--lp` or `--file`: The line protocol to test - Optional: `--input-arguments`: A comma-delimited list of `=` arguments for your plugin code ```bash influxdb3 test wal_plugin \ --lp \ --input-arguments "arg1=foo,arg2=bar" \ --database \ ``` The command runs the plugin code with the test data, yields the data to the plugin code, and then responds with the plugin result. You can quickly see how the plugin behaves, what data it would have written to the database, and any errors. You can then edit your Python code in the plugins directory, and rerun the test. The server reloads the file for every request to the `test` API. For more information, see [`influxdb3 test wal_plugin`](/influxdb3/version/reference/cli/influxdb3/test/wal_plugin/) or run `influxdb3 test wal_plugin -h`. With the plugin code inside the server plugin directory, and a successful test, you're ready to create a plugin and a trigger to run on the server. ##### Example: Test, create, and run a plugin The following example shows how to test a plugin, and then create the plugin and trigger: ```bash # Test and create a plugin # Requires: # - A database named `mydb` with a table named `foo` # - A Python plugin file named `test.py` # Test a plugin influxdb3 test wal_plugin \ --lp "my_measure,tag1=asdf f1=1.0 123" \ --database mydb \ --input-arguments "arg1=hello,arg2=world" \ test.py ``` ```bash # Create a trigger that runs the plugin influxdb3 create trigger \ -d mydb \ --plugin test_plugin \ --trigger-spec "table:foo" \ --trigger-arguments "arg1=hello,arg2=world" \ trigger1 ``` After you have created a plugin and trigger, enter the following command to enable the trigger and have it run the plugin as you write data: ```bash influxdb3 enable trigger --database mydb trigger1 ``` For more information, see [Python plugins and the Processing engine](/influxdb3/version/plugins/). ### Multi-server setup {{% product-name %}} is built to support multi-node setups for high availability, read replicas, and flexible implementations depending on use case. ### High availability Enterprise is architecturally flexible, giving you options on how to configure multiple servers that work together for high availability (HA) and high performance. Built on top of the diskless engine and leveraging the Object store, an HA setup ensures that if a node fails, you can still continue reading from, and writing to, a secondary node. A two-node setup is the minimum for basic high availability, with both nodes having read-write permissions. {{< img-hd src="/img/influxdb/influxdb-3-enterprise-high-availability.png" alt="Basic high availability setup" />}} In a basic HA setup: - Two nodes both write data to the same Object store and both handle queries - Node 1 and Node 2 are _read replicas_ that read from each other’s Object store directories - One of the nodes is designated as the Compactor node > [!Note] > Only one node can be designated as the Compactor. > Compacted data is meant for a single writer, and many readers. The following examples show how to configure and start two nodes for a basic HA setup. - _Node 1_ is for compaction (passes `compact` in `--mode`) - _Node 2_ is for ingest and query ```bash ## NODE 1 # Example variables # node-id: 'host01' # cluster-id: 'cluster01' # bucket: 'influxdb-3-enterprise-storage' influxdb3 serve \ --node-id host01 \ --cluster-id cluster01 \ --mode ingest,query,compact \ --object-store s3 \ --bucket influxdb-3-enterprise-storage \ --http-bind {{< influxdb/host >}} \ --aws-access-key-id \ --aws-secret-access-key ```bash ## NODE 2 # Example variables # node-id: 'host02' # cluster-id: 'cluster01' # bucket: 'influxdb-3-enterprise-storage' influxdb3 serve \ --node-id host02 \ --cluster-id cluster01 \ --mode ingest,query \ --object-store s3 \ --bucket influxdb-3-enterprise-storage \ --http-bind localhost:8282 \ --aws-access-key-id \ --aws-secret-access-key ``` After the nodes have started, querying either node returns data for both nodes, and _NODE 1_ runs compaction. To add nodes to this setup, start more read replicas with the same cluster ID. ### High availability with a dedicated Compactor Data compaction in InfluxDB 3 is one of the more computationally expensive operations. To ensure that your read-write nodes don't slow down due to compaction work, set up a compactor-only node for consistent and high performance across all nodes. {{< img-hd src="/img/influxdb/influxdb-3-enterprise-dedicated-compactor.png" alt="Dedicated Compactor setup" />}} The following examples show how to set up high availability with a dedicated Compactor node: 1. Start two read-write nodes as read replicas, similar to the previous example. ```bash ## NODE 1 — Writer/Reader Node #1 # Example variables # node-id: 'host01' # cluster-id: 'cluster01' # bucket: 'influxdb-3-enterprise-storage' influxdb3 serve \ --node-id host01 \ --cluster-id cluster01 \ --mode ingest,query \ --object-store s3 \ --bucket influxdb-3-enterprise-storage \ --http-bind {{< influxdb/host >}} \ --aws-access-key-id \ --aws-secret-access-key ``` ```bash ## NODE 2 — Writer/Reader Node #2 # Example variables # node-id: 'host02' # cluster-id: 'cluster01' # bucket: 'influxdb-3-enterprise-storage' influxdb3 serve \ --node-id host02 \ --cluster-id cluster01 \ --mode ingest,query \ --object-store s3 \ --bucket influxdb-3-enterprise-storage \ --http-bind localhost:8282 \ --aws-access-key-id \ --aws-secret-access-key ``` 2. Start the dedicated compactor node with the `--mode=compact` option to ensure the node **only** runs compaction. ```bash ## NODE 3 — Compactor Node # Example variables # node-id: 'host03' # cluster-id: 'cluster01' # bucket: 'influxdb-3-enterprise-storage' influxdb3 serve \ --node-id host03 \ --cluster-id cluster01 \ --mode compact \ --object-store s3 \ --bucket influxdb-3-enterprise-storage \ --aws-access-key-id \ --aws-secret-access-key ``` ### High availability with read replicas and a dedicated Compactor For a robust and effective setup for managing time-series data, you can run ingest nodes alongside read-only nodes and a dedicated Compactor node. {{< img-hd src="/img/influxdb/influxdb-3-enterprise-workload-isolation.png" alt="Workload Isolation Setup" />}} 1. Start ingest nodes by assigning them the **`ingest`** mode. To achieve the benefits of workload isolation, you'll send _only write requests_ to these ingest nodes. Later, you'll configure the _read-only_ nodes. ```bash ## NODE 1 — Writer Node #1 # Example variables # node-id: 'host01' # cluster-id: 'cluster01' # bucket: 'influxdb-3-enterprise-storage' influxdb3 serve \ --node-id host01 \ --cluster-id cluster01 \ --mode ingest \ --object-store s3 \ --bucket influxdb-3-enterprise-storage \ -- http-bind {{< influxdb/host >}} \ --aws-access-key-id \ --aws-secret-access-key ``` ```bash ## NODE 2 — Writer Node #2 # Example variables # node-id: 'host02' # cluster-id: 'cluster01' # bucket: 'influxdb-3-enterprise-storage' influxdb3 serve \ --node-id host02 \ --cluster-id cluster01 \ --mode ingest \ --object-store s3 \ --bucket influxdb-3-enterprise-storage \ --http-bind localhost:8282 \ --aws-access-key-id \ --aws-secret-access-key ``` 2. Start the dedicated Compactor node with ` compact`. ```bash ## NODE 3 — Compactor Node # Example variables # node-id: 'host03' # cluster-id: 'cluster01' # bucket: 'influxdb-3-enterprise-storage' influxdb3 serve \ --node-id host03 \ --cluster-id cluster01 \ --mode compact \ --object-store s3 \ --bucket influxdb-3-enterprise-storage \ --aws-access-key-id \ ``` 3. Finally, start the query nodes as _read-only_ with `--mode query`. ```bash ## NODE 4 — Read Node #1 # Example variables # node-id: 'host04' # cluster-id: 'cluster01' # bucket: 'influxdb-3-enterprise-storage' influxdb3 serve \ --node-id host04 \ --cluster-id cluster01 \ --mode query \ --object-store s3 \ --bucket influxdb-3-enterprise-storage \ -- http-bind localhost:8383 \ --aws-access-key-id \ --aws-secret-access-key ``` ```bash ## NODE 5 — Read Node #2 # Example variables # node-id: 'host05' # cluster-id: 'cluster01' # bucket: 'influxdb-3-enterprise-storage' influxdb3 serve \ --node-id host05 \ --cluster-id cluster01 \ --mode query \ --object-store s3 \ --bucket influxdb-3-enterprise-storage \ -- http-bind localhost:8484 \ --aws-access-key-id \ ``` Congratulations, you have a robust setup for workload isolation using {{% product-name %}}. ### Writing and querying for multi-node setups You can use the default port `8181` for any write or query, without changing any of the commands. > [!Note] > #### Specify hosts for writes and queries > > To benefit from this multi-node, isolated architecture, specify hosts: > > - In write requests, specify a host that you have designated as _write-only_. > - In query requests, specify a host that you have designated as _read-only_. > > When running multiple local instances for testing or separate nodes in production, specifying the host ensures writes and queries are routed to the correct instance. ```bash # Example variables on a query # HTTP-bound Port: 8585 influxdb3 query http://localhost:8585 --database "" ``` ### File index settings To accelerate performance on specific queries, you can define non-primary keys to index on, which helps improve performance for single-series queries. This feature is only available in Enterprise and is not available in Core. #### Create a file index ```bash # Example variables on a query # HTTP-bound Port: 8585 influxdb3 create file_index \ --host http://localhost:8585 \ --database \ --table
\ ``` #### Delete a file index ```bash influxdb3 delete file_index \ --host http://localhost:8585 \ --database \ --table
\ ```