Syslog and Clickhouse Integration
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Table of Contents
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 Syslog plugin enables the collection of syslog messages from various sources using standard networking protocols. This functionality is critical for environments where systems need to be monitored and logged efficiently.
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
Syslog
The Syslog plugin for Telegraf captures syslog messages transmitted over various protocols such as TCP, UDP, and TLS. It supports both RFC 5424 (the newer syslog protocol) and the older RFC 3164 (BSD syslog protocol). This plugin operates as a service input, effectively starting a service that listens for incoming syslog messages. Unlike traditional plugins, service inputs may not function with standard interval settings or CLI options like --once
. It includes options for setting network configurations, socket permissions, message handling, and connection handling. Furthermore, the integration with Rsyslog allows forwarding of logging messages, making it a powerful tool for collecting and relaying system logs in real-time, thus seamlessly integrating into monitoring and logging systems.
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
Syslog
[[inputs.syslog]]
## Protocol, address and port to host the syslog receiver.
## If no host is specified, then localhost is used.
## If no port is specified, 6514 is used (RFC5425#section-4.1).
## ex: server = "tcp://localhost:6514"
## server = "udp://:6514"
## server = "unix:///var/run/telegraf-syslog.sock"
## When using tcp, consider using 'tcp4' or 'tcp6' to force the usage of IPv4
## or IPV6 respectively. There are cases, where when not specified, a system
## may force an IPv4 mapped IPv6 address.
server = "tcp://127.0.0.1:6514"
## Permission for unix sockets (only available on unix sockets)
## This setting may not be respected by some platforms. To safely restrict
## permissions it is recommended to place the socket into a previously
## created directory with the desired permissions.
## ex: socket_mode = "777"
# socket_mode = ""
## Maximum number of concurrent connections (only available on stream sockets like TCP)
## Zero means unlimited.
# max_connections = 0
## Read timeout (only available on stream sockets like TCP)
## Zero means unlimited.
# read_timeout = "0s"
## Optional TLS configuration (only available on stream sockets like TCP)
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Enables client authentication if set.
# tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]
## Maximum socket buffer size (in bytes when no unit specified)
## For stream sockets, once the buffer fills up, the sender will start
## backing up. For datagram sockets, once the buffer fills up, metrics will
## start dropping. Defaults to the OS default.
# read_buffer_size = "64KiB"
## Period between keep alive probes (only applies to TCP sockets)
## Zero disables keep alive probes. Defaults to the OS configuration.
# keep_alive_period = "5m"
## Content encoding for message payloads
## Can be set to "gzip" for compressed payloads or "identity" for no encoding.
# content_encoding = "identity"
## Maximum size of decoded packet (in bytes when no unit specified)
# max_decompression_size = "500MB"
## Framing technique used for messages transport
## Available settings are:
## octet-counting -- see RFC5425#section-4.3.1 and RFC6587#section-3.4.1
## non-transparent -- see RFC6587#section-3.4.2
# framing = "octet-counting"
## The trailer to be expected in case of non-transparent framing (default = "LF").
## Must be one of "LF", or "NUL".
# trailer = "LF"
## Whether to parse in best effort mode or not (default = false).
## By default best effort parsing is off.
# best_effort = false
## The RFC standard to use for message parsing
## By default RFC5424 is used. RFC3164 only supports UDP transport (no streaming support)
## Must be one of "RFC5424", or "RFC3164".
# syslog_standard = "RFC5424"
## Character to prepend to SD-PARAMs (default = "_").
## A syslog message can contain multiple parameters and multiple identifiers within structured data section.
## Eg., [id1 name1="val1" name2="val2"][id2 name1="val1" nameA="valA"]
## For each combination a field is created.
## Its name is created concatenating identifier, sdparam_separator, and parameter name.
# sdparam_separator = "_"
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
Syslog
-
Centralized Log Management: Use the Syslog plugin to aggregate log messages from multiple servers into a central logging system. This setup can help in monitoring overall system health, troubleshooting issues effectively, and maintaining audit trails by collecting syslog data from different sources.
-
Real-Time Alerting: Integrate the Syslog plugin with alerting tools to trigger real-time notifications when specific log patterns or errors are detected. For example, if a critical system error appears in the logs, an alert can be sent to the operations team, minimizing downtime and performing proactive maintenance.
-
Security Monitoring: Leverage the Syslog plugin for security monitoring by capturing logs from firewalls, intrusion detection systems, and other security devices. This logging capability enhances security visibility and helps in investigating potentially malicious activities by analyzing the captured syslog data.
-
Application Performance Tracking: Utilize the Syslog plugin to monitor application performance by collecting logs from various applications. This integration helps in analyzing the application’s behavior and performance trends, thus aiding in optimizing application processes and ensuring smoother operation.
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.
-
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.
-
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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