All Integrations / Google Cloud Stackdriver and TimescaleDB Integration

Google Cloud Stackdriver and TimescaleDB Integration

Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.

Get This Integration for Free
info

This is not the recommended configuration for real-time query at scale. For query and compression optimization, high-speed ingest, and high availability, you may want to consider <a href="/integrations/stackdriver-influxdb/">Stackdriver and InfluxDB</a>.

5B+

Telegraf downloads

#1

Time series database
Source: DB Engines

1B+

Downloads of InfluxDB

2,800+

Contributors

Table of Contents

Useful Links

Documentation

Telegraf Quickstart Training

Infrastructure Monitoring with Telegraf

Powerful Performance, Limitless Scale

Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.

See Ways to Get Started

Input and output integration overview

<p>This plugin enables the collection of monitoring data from Google Cloud services through the Stackdriver Monitoring API. It is designed to help users monitor their cloud infrastructure’s performance and health by gathering relevant metrics.</p>

<p>This output plugin delivers a reliable and efficient mechanism for routing Telegraf collected metrics directly into TimescaleDB. By leveraging PostgreSQL’s robust ecosystem combined with TimescaleDB’s time series optimizations, it supports high-performance data ingestion and advanced querying capabilities.</p>

Integration details

Google Cloud Stackdriver

<p>The Stackdriver Telegraf plugin allows users to query timeseries data from Google Cloud Monitoring using the Cloud Monitoring API v3. With this plugin, users can easily integrate Google Cloud monitoring metrics into their monitoring stacks. This API provides a wealth of insights about resources and applications running in Google Cloud, including performance, uptime, and operational metrics. The plugin supports various configuration options to filter and refine the data retrieved, enabling users to customize their monitoring setup according to their specific needs. This integration facilitates a smoother experience in maintaining the health and performance of cloud resources and assists teams in making data-driven decisions based on historical and current performance statistics.</p>

TimescaleDB

<p>TimescaleDB is an open source time series database built as an extension to PostgreSQL, designed to handle large scale, time-oriented data efficiently. Launched in 2017, TimescaleDB emerged in response to the growing need for a robust, scalable solution that could manage vast volumes of data with high insert rates and complex queries. By leveraging PostgreSQL’s familiar SQL interface and enhancing it with specialized time series capabilities, TimescaleDB quickly gained popularity among developers looking to integrate time series functionality into existing relational databases. Its hybrid approach allows users to benefit from PostgreSQL’s flexibility, reliability, and ecosystem while providing optimized performance for time series data.</p> <p>The database is particularly effective in environments that demand fast ingestion of data points combined with sophisticated analytical queries over historical periods. TimescaleDB has a number of innovative features like hypertables which transparently partition data into manageable chunks and built-in continuous aggregation. These allow for significantly improved query speed and resource efficiency.</p>

Configuration

Google Cloud Stackdriver

<pre><code class="language-toml">[[inputs.stackdriver]] ## GCP Project project = "erudite-bloom-151019" ## Include timeseries that start with the given metric type. metric_type_prefix_include = [ "compute.googleapis.com/", ] ## Exclude timeseries that start with the given metric type. # metric_type_prefix_exclude = [] ## Most metrics are updated no more than once per minute; it is recommended ## to override the agent level interval with a value of 1m or greater. interval = "1m" ## Maximum number of API calls to make per second. The quota for accounts ## varies, it can be viewed on the API dashboard: ## https://cloud.google.com/monitoring/quotas#quotas_and_limits # rate_limit = 14 ## The delay and window options control the number of points selected on ## each gather. When set, metrics are gathered between: ## start: now() - delay - window ## end: now() - delay # ## Collection delay; if set too low metrics may not yet be available. # delay = "5m" # ## If unset, the window will start at 1m and be updated dynamically to span ## the time between calls (approximately the length of the plugin interval). # window = "1m" ## TTL for cached list of metric types. This is the maximum amount of time ## it may take to discover new metrics. # cache_ttl = "1h" ## If true, raw bucket counts are collected for distribution value types. ## For a more lightweight collection, you may wish to disable and use ## distribution_aggregation_aligners instead. # gather_raw_distribution_buckets = true ## Aggregate functions to be used for metrics whose value type is ## distribution. These aggregate values are recorded in in addition to raw ## bucket counts; if they are enabled. ## ## For a list of aligner strings see: ## https://cloud.google.com/monitoring/api/ref_v3/rpc/google.monitoring.v3#aligner # distribution_aggregation_aligners = [ # "ALIGN_PERCENTILE_99", # "ALIGN_PERCENTILE_95", # "ALIGN_PERCENTILE_50", # ] ## Filters can be added to reduce the number of time series matched. All ## functions are supported: starts_with, ends_with, has_substring, and ## one_of. Only the '=' operator is supported. ## ## The logical operators when combining filters are defined statically using ## the following values: ## filter ::= <resource_labels> {AND <metric_labels> AND <user_labels> AND <system_labels>} ## resource_labels ::= <resource_labels> {OR <resource_label>} ## metric_labels ::= <metric_labels> {OR <metric_label>} ## user_labels ::= <user_labels> {OR <user_label>} ## system_labels ::= <system_labels> {OR <system_label>} ## ## For more details, see https://cloud.google.com/monitoring/api/v3/filters # ## Resource labels refine the time series selection with the following expression: ## resource.labels.<key> = <value> # [[inputs.stackdriver.filter.resource_labels]] # key = "instance_name" # value = 'starts_with("localhost")' # ## Metric labels refine the time series selection with the following expression: ## metric.labels.<key> = <value> # [[inputs.stackdriver.filter.metric_labels]] # key = "device_name" # value = 'one_of("sda", "sdb")' # ## User labels refine the time series selection with the following expression: ## metadata.user_labels."<key>" = <value> # [[inputs.stackdriver.filter.user_labels]] # key = "environment" # value = 'one_of("prod", "staging")' # ## System labels refine the time series selection with the following expression: ## metadata.system_labels."<key>" = <value> # [[inputs.stackdriver.filter.system_labels]] # key = "machine_type" # value = 'starts_with("e2-")' &lt;/code&gt;&lt;/pre&gt; </value></key></value></key></value></key></value></key></system_label></system_labels></user_label></user_labels></metric_label></metric_labels></resource_label></resource_labels></system_labels></user_labels></metric_labels></resource_labels></code></pre>

TimescaleDB

<pre><code class="language-toml"># Publishes metrics to a TimescaleDB database [[outputs.postgresql]] ## Specify connection address via the standard libpq connection string: ## host=... user=... password=... sslmode=... dbname=... ## Or a URL: ## postgres://[user[:password]]@localhost[/dbname]?sslmode=[disable|verify-ca|verify-full] ## See https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING ## ## All connection parameters are optional. Environment vars are also supported. ## e.g. PGPASSWORD, PGHOST, PGUSER, PGDATABASE ## All supported vars can be found here: ## https://www.postgresql.org/docs/current/libpq-envars.html ## ## Non-standard parameters: ## pool_max_conns (default: 1) - Maximum size of connection pool for parallel (per-batch per-table) inserts. ## pool_min_conns (default: 0) - Minimum size of connection pool. ## pool_max_conn_lifetime (default: 0s) - Maximum connection age before closing. ## pool_max_conn_idle_time (default: 0s) - Maximum idle time of a connection before closing. ## pool_health_check_period (default: 0s) - Duration between health checks on idle connections. # connection = "" ## Postgres schema to use. # schema = "public" ## Store tags as foreign keys in the metrics table. Default is false. # tags_as_foreign_keys = false ## Suffix to append to table name (measurement name) for the foreign tag table. # tag_table_suffix = "_tag" ## Deny inserting metrics if the foreign tag can't be inserted. # foreign_tag_constraint = false ## Store all tags as a JSONB object in a single 'tags' column. # tags_as_jsonb = false ## Store all fields as a JSONB object in a single 'fields' column. # fields_as_jsonb = false ## Name of the timestamp column ## NOTE: Some tools (e.g. Grafana) require the default name so be careful! # timestamp_column_name = "time" ## Type of the timestamp column ## Currently, "timestamp without time zone" and "timestamp with time zone" ## are supported # timestamp_column_type = "timestamp without time zone" ## Templated statements to execute when creating a new table. # create_templates = [ # '''CREATE TABLE {{ .table }} ({{ .columns }})''', # ] ## Templated statements to execute when adding columns to a table. ## Set to an empty list to disable. Points containing tags for which there is ## no column will be skipped. Points containing fields for which there is no ## column will have the field omitted. # add_column_templates = [ # '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''', # ] ## Templated statements to execute when creating a new tag table. # tag_table_create_templates = [ # '''CREATE TABLE {{ .table }} ({{ .columns }}, PRIMARY KEY (tag_id))''', # ] ## Templated statements to execute when adding columns to a tag table. ## Set to an empty list to disable. Points containing tags for which there is ## no column will be skipped. # tag_table_add_column_templates = [ # '''ALTER TABLE {{ .table }} ADD COLUMN IF NOT EXISTS {{ .columns|join ", ADD COLUMN IF NOT EXISTS " }}''', # ] ## The postgres data type to use for storing unsigned 64-bit integer values ## (Postgres does not have a native unsigned 64-bit integer type). ## The value can be one of: ## numeric - Uses the PostgreSQL "numeric" data type. ## uint8 - Requires pguint extension (https://github.com/petere/pguint) # uint64_type = "numeric" ## When using pool_max_conns &gt; 1, and a temporary error occurs, the query is ## retried with an incremental backoff. This controls the maximum duration. # retry_max_backoff = "15s" ## Approximate number of tag IDs to store in in-memory cache (when using ## tags_as_foreign_keys). This is an optimization to skip inserting known ## tag IDs. Each entry consumes approximately 34 bytes of memory. # tag_cache_size = 100000 ## Cut column names at the given length to not exceed PostgreSQL's ## 'identifier length' limit (default: no limit) ## (see https://www.postgresql.org/docs/current/limits.html) ## Be careful to not create duplicate column names! # column_name_length_limit = 0 ## Enable &amp; set the log level for the Postgres driver. # log_level = "warn" # trace, debug, info, warn, error, none </code></pre>

Input and output integration examples

Google Cloud Stackdriver

<ol> <li> <p><strong>Integrating Cloud Metrics into Custom Dashboards</strong>: With this plugin, teams can funnel metrics from Google Cloud into personalized dashboards, allowing for real-time monitoring of application performance and resource utilization. By customizing the visual representation of cloud metrics, operations teams can easily identify trends and anomalies, enabling proactive management before issues escalate.</p> </li> <li> <p><strong>Automated Alerts and Analysis</strong>: Users can set up automated alerting mechanisms leveraging the plugin’s metrics to track resource thresholds. This capability allows teams to act swiftly in response to performance degradation or outages by providing immediate notifications, thus reducing the mean time to recovery and ensuring continued operational efficiency.</p> </li> <li> <p><strong>Cross-Platform Resource Comparison</strong>: The plugin can be used to draw metrics from various Google Cloud services and compare them with on-premise resources. This cross-platform visibility helps organizations make informed decisions about resource allocation and scaling strategies, as well as optimize cloud spending versus on-premise infrastructure.</p> </li> <li> <p><strong>Historical Data Analysis for Capacity Planning</strong>: By collecting historical metrics over time, the plugin empowers teams to conduct thorough capacity planning. Understanding past performance trends facilitates accurate forecasting for resource needs, leading to better budgeting and investment strategies.</p> </li> </ol>

TimescaleDB

<ol> <li> <p><strong>Real-Time IoT Data Ingestion</strong>: Use the plugin to collect and store sensor data from thousands of IoT devices in real time. This setup facilitates immediate analysis, helping organizations monitor operational efficiency and respond quickly to changing conditions.</p> </li> <li> <p><strong>Cloud Application Performance Monitoring</strong>: Leverage the plugin to feed detailed performance metrics from distributed cloud applications into TimescaleDB. This integration supports real-time dashboards and alerts, enabling teams to swiftly identify and mitigate performance bottlenecks.</p> </li> <li> <p><strong>Historical Data Analysis and Reporting</strong>: Implement a system where long-term metrics are stored in TimescaleDB for comprehensive historical analysis. This approach allows businesses to perform trend analysis, generate detailed reports, and make data-driven decisions based on archived time-series data.</p> </li> <li> <p><strong>Adaptive Alerting and Anomaly Detection</strong>: Integrate the plugin with automated anomaly detection workflows. By continuously streaming metrics to TimescaleDB, machine learning models can analyze data patterns and trigger alerts when anomalies occur, enhancing system reliability and proactive maintenance.</p> </li> </ol>

Feedback

Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.

Useful Links

Documentation

Telegraf Quickstart Training

Infrastructure Monitoring with Telegraf

Powerful Performance, Limitless Scale

Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.

See Ways to Get Started

Related Integrations

HTTP and InfluxDB Integration

The HTTP plugin collects metrics from one or more HTTP(S) endpoints. It supports various authentication methods and configuration options for data formats.

View Integration

Kafka and InfluxDB Integration

This plugin reads messages from Kafka and allows the creation of metrics based on those messages. It supports various configurations including different Kafka settings and message processing options.

View Integration

Kinesis and InfluxDB Integration

The Kinesis plugin allows for reading metrics from AWS Kinesis streams. It supports multiple input data formats and offers checkpointing features with DynamoDB for reliable message processing.

View Integration

Start building now

Download Telegraf today or learn more about InfluxDB.

telegraf Get started with InfluxDB
Product & Solutions
Developers
Company

548 Market St, PMB 77953
San Francisco, California 94104

© 2025 InfluxData Inc. All Rights Reserved.

Legal Security Cookie Policy Comparison