Docker and Clickhouse Integration
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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
The Docker input plugin allows you to collect metrics from your Docker containers using the Docker Engine API, facilitating enhanced visibility and monitoring of containerized applications.
Telegraf’s SQL plugin sends collected metrics to an SQL database using a straightforward table schema and dynamic column generation. When configured for ClickHouse, it adjusts DSN formatting and type conversion settings to ensure seamless data integration.
Integration details
Docker
The Docker input plugin for Telegraf gathers valuable metrics from the Docker Engine API, providing insights into running containers. This plugin utilizes the Official Docker Client to interface with the Engine API, allowing users to monitor various container states, resource allocations, and performance metrics. With options for filtering containers by names and states, along with customizable tags and labels, this plugin supports flexibility in monitoring containerized applications in diverse environments, whether on local systems or within orchestration platforms like Kubernetes. Additionally, it addresses security considerations by requiring permissions for accessing Docker’s daemon and emphasizes proper configuration when deploying within containerized environments.
Clickhouse
Telegraf’s SQL plugin is engineered to write metric data into an SQL database by dynamically creating tables and columns based on incoming metrics. When configured for ClickHouse, it utilizes the clickhouse-go v1.5.4 driver, which employs a unique DSN format and a set of specialized type conversion rules to map Telegraf’s data types directly to ClickHouse’s native types. This approach ensures optimal storage and retrieval performance in high-throughput environments, making it well-suited for real-time analytics and large-scale data warehousing. The dynamic schema creation and precise type mapping enable detailed time-series data logging, crucial for monitoring modern, distributed systems.
Configuration
Docker
[[inputs.docker]]
## Docker Endpoint
## To use TCP, set endpoint = "tcp://[ip]:[port]"
## To use environment variables (ie, docker-machine), set endpoint = "ENV"
endpoint = "unix:///var/run/docker.sock"
## Set to true to collect Swarm metrics(desired_replicas, running_replicas)
## Note: configure this in one of the manager nodes in a Swarm cluster.
## configuring in multiple Swarm managers results in duplication of metrics.
gather_services = false
## Only collect metrics for these containers. Values will be appended to
## container_name_include.
## Deprecated (1.4.0), use container_name_include
container_names = []
## Set the source tag for the metrics to the container ID hostname, eg first 12 chars
source_tag = false
## Containers to include and exclude. Collect all if empty. Globs accepted.
container_name_include = []
container_name_exclude = []
## Container states to include and exclude. Globs accepted.
## When empty only containers in the "running" state will be captured.
# container_state_include = []
# container_state_exclude = []
## Objects to include for disk usage query
## Allowed values are "container", "image", "volume"
## When empty disk usage is excluded
storage_objects = []
## Timeout for docker list, info, and stats commands
timeout = "5s"
## Whether to report for each container per-device blkio (8:0, 8:1...),
## network (eth0, eth1, ...) and cpu (cpu0, cpu1, ...) stats or not.
## Usage of this setting is discouraged since it will be deprecated in favor of 'perdevice_include'.
## Default value is 'true' for backwards compatibility, please set it to 'false' so that 'perdevice_include' setting
## is honored.
perdevice = true
## Specifies for which classes a per-device metric should be issued
## Possible values are 'cpu' (cpu0, cpu1, ...), 'blkio' (8:0, 8:1, ...) and 'network' (eth0, eth1, ...)
## Please note that this setting has no effect if 'perdevice' is set to 'true'
# perdevice_include = ["cpu"]
## Whether to report for each container total blkio and network stats or not.
## Usage of this setting is discouraged since it will be deprecated in favor of 'total_include'.
## Default value is 'false' for backwards compatibility, please set it to 'true' so that 'total_include' setting
## is honored.
total = false
## Specifies for which classes a total metric should be issued. Total is an aggregated of the 'perdevice' values.
## Possible values are 'cpu', 'blkio' and 'network'
## Total 'cpu' is reported directly by Docker daemon, and 'network' and 'blkio' totals are aggregated by this plugin.
## Please note that this setting has no effect if 'total' is set to 'false'
# total_include = ["cpu", "blkio", "network"]
## docker labels to include and exclude as tags. Globs accepted.
## Note that an empty array for both will include all labels as tags
docker_label_include = []
docker_label_exclude = []
## Which environment variables should we use as a tag
tag_env = ["JAVA_HOME", "HEAP_SIZE"]
## Optional TLS Config
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
Clickhouse
[[outputs.sql]]
## Database driver
## Valid options include mssql, mysql, pgx, sqlite, snowflake, clickhouse
driver = "clickhouse"
## Data source name
## For ClickHouse, the DSN follows the clickhouse-go v1.5.4 format.
## Example DSN: "tcp://localhost:9000?debug=true"
data_source_name = "tcp://localhost:9000?debug=true"
## Timestamp column name
timestamp_column = "timestamp"
## Table creation template
## Available template variables:
## {TABLE} - table name as a quoted identifier
## {TABLELITERAL} - table name as a quoted string literal
## {COLUMNS} - column definitions (list of quoted identifiers and types)
table_template = "CREATE TABLE {TABLE} ({COLUMNS})"
## Table existence check template
## Available template variables:
## {TABLE} - table name as a quoted identifier
table_exists_template = "SELECT 1 FROM {TABLE} LIMIT 1"
## Initialization SQL (optional)
init_sql = ""
## Maximum amount of time a connection may be idle. "0s" means connections are never closed due to idle time.
connection_max_idle_time = "0s"
## Maximum amount of time a connection may be reused. "0s" means connections are never closed due to age.
connection_max_lifetime = "0s"
## Maximum number of connections in the idle connection pool. 0 means unlimited.
connection_max_idle = 2
## Maximum number of open connections to the database. 0 means unlimited.
connection_max_open = 0
## Metric type to SQL type conversion for ClickHouse.
## The conversion maps Telegraf metric types to ClickHouse native data types.
[outputs.sql.convert]
conversion_style = "literal"
integer = "Int64"
text = "String"
timestamp = "DateTime"
defaultvalue = "String"
unsigned = "UInt64"
bool = "UInt8"
real = "Float64"
Input and output integration examples
Docker
-
Monitoring the Performance of Containerized Applications: Use the Docker input plugin in order to track the CPU, memory, disk I/O, and network activity of applications running in Docker containers. By collecting these metrics, DevOps teams can proactively manage resource allocation, troubleshoot performance bottlenecks, and ensure optimal application performance across different environments.
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Integrating with Kubernetes: Leverage this plugin to gather metrics from Docker containers orchestrated by Kubernetes. By filtering out unnecessary Kubernetes labels and focusing on key metrics, teams can streamline their monitoring solutions and create dashboards that provide insights into the overall health of microservices running within the Kubernetes cluster.
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Capacity Planning and Resource Optimization: Use the metrics collected by the Docker input plugin to perform capacity planning for Docker deployments. Analyzing usage patterns helps identify underutilized resources and over-provisioned containers, guiding decisions on scaling up or down based on actual usage trends.
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Automated Alerting for Container Anomalies: Set up alerting rules based on the metrics collected through the Docker plugin to notify teams of unusual spikes in resource usage or service disruptions. This proactive monitoring approach helps maintain service reliability and optimize the performance of containerized applications.
Clickhouse
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Real-Time Analytics for High-Volume Data: Use the plugin to feed streaming metrics from large-scale systems into ClickHouse. This setup supports ultra-fast query performance and near real-time analytics, ideal for monitoring high-traffic applications.
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Time-Series Data Warehousing: Integrate the plugin with ClickHouse to create a robust time-series data warehouse. This use case allows organizations to store detailed historical metrics and perform complex queries for trend analysis and capacity planning.
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Scalable Monitoring in Distributed Environments: Leverage the plugin to dynamically create tables per metric type in ClickHouse, making it easier to manage and query data from a multitude of distributed systems without prior schema definitions.
-
Optimized Storage for IoT Deployments: Deploy the plugin to ingest data from IoT sensors into ClickHouse. Its efficient schema creation and native type mapping facilitate the handling of massive volumes of data, enabling real-time monitoring and predictive maintenance.
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.
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
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