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syslog-ng Premium Edition 7.0.31 - Administration Guide

Preface Introduction to syslog-ng The concepts of syslog-ng Installing syslog-ng PE The syslog-ng PE quick-start guide The syslog-ng PE configuration file Collecting log messages — sources and source drivers
How sources work default-network-drivers: Receive and parse common syslog messages internal: Collecting internal messages file: Collecting messages from text files google-pubsub: collecting messages from the Google Pub/Sub messaging service wildcard-file: Collecting messages from multiple text files linux-audit: Collecting messages from Linux audit logs mssql, oracle, sql: collecting messages from an SQL database network: Collecting messages using the RFC3164 protocol (network() driver) office365: Fetching logs from Office 365 osquery: Collect and parse osquery result logs pipe: Collecting messages from named pipes program: Receiving messages from external applications python: writing server-style Python sources python-fetcher: writing fetcher-style Python sources snmptrap: Read Net-SNMP traps syslog: Collecting messages using the IETF syslog protocol (syslog() driver) system: Collecting the system-specific log messages of a platform systemd-journal: Collecting messages from the systemd-journal system log storage systemd-syslog: Collecting systemd messages using a socket tcp, tcp6,udp, udp6: Collecting messages from remote hosts using the BSD syslog protocol udp-balancer: Receiving UDP messages at very high rate unix-stream, unix-dgram: Collecting messages from UNIX domain sockets windowsevent: Collecting Windows event logs
Sending and storing log messages — destinations and destination drivers
elasticsearch2>: Sending messages directly to Elasticsearch version 2.0 or higher (DEPRECATED) elasticsearch-http: Sending messages to Elasticsearch HTTP Event Collector file: Storing messages in plain-text files google_pubsub(): Sending logs to the Google Cloud Pub/Sub messaging service hdfs: Storing messages on the Hadoop Distributed File System (HDFS) http: Posting messages over HTTP kafka(): Publishing messages to Apache Kafka (Java implementation) (DEPRECATED) kafka-c(): Publishing messages to Apache Kafka using the librdkafka client (C implementation) logstore: Storing messages in encrypted files mongodb: Storing messages in a MongoDB database network: Sending messages to a remote log server using the RFC3164 protocol (network() driver) pipe: Sending messages to named pipes program: Sending messages to external applications python: writing custom Python destinations sentinel(): Sending logs to the Microsoft Azure Sentinel cloud snmp: Sending SNMP traps smtp: Generating SMTP messages (email) from logs splunk-hec: Sending messages to Splunk HTTP Event Collector sql(): Storing messages in an SQL database stackdriver: Sending logs to the Google Stackdriver cloud syslog: Sending messages to a remote logserver using the IETF-syslog protocol syslog-ng(): Forward logs to another syslog-ng node tcp, tcp6, udp, udp6: Sending messages to a remote log server using the legacy BSD-syslog protocol (tcp(), udp() drivers) unix-stream, unix-dgram: Sending messages to UNIX domain sockets usertty: Sending messages to a user terminal — usertty() destination Client-side failover
Routing messages: log paths, flags, and filters Global options of syslog-ng PE TLS-encrypted message transfer Advanced Log Transport Protocol Reliability and minimizing the loss of log messages Manipulating messages parser: Parse and segment structured messages Processing message content with a pattern database Correlating log messages Enriching log messages with external data Monitoring statistics and metrics of syslog-ng Multithreading and scaling in syslog-ng PE Troubleshooting syslog-ng Best practices and examples The syslog-ng manual pages Glossary

Using pattern databases

To classify messages using a pattern database, include a db-parser() statement in your syslog-ng configuration file using the following syntax:

parser <identifier> {db-parser(file("<database_filename>"));};

Note that using the parser in a log statement only performs the classification, but does not automatically do anything with the results of the classification.

Example: Defining pattern databases

The following statement uses the database located at /opt/syslog-ng/var/db/patterndb.xml.

parser pattern_db {

To apply the patterns on the incoming messages, include the parser in a log statement:

log {
        destination( di_messages_class);

NOTE: The default location of the pattern database file is /opt/syslog-ng/var/run/patterndb.xml. The file option of the db-parser() statement can be used to specify a different file, thus different db-parser statements can use different pattern databases. Later versions of syslog-ng will be able to dynamically generate a main database from separate pattern database files.

Example: Using classification results

The following destination separates the log messages into different files based on the class assigned to the pattern that matches the message (for example, Violation and Security type messages are stored in a separate file), and also adds the ID of the matching rule to the message:

destination di_messages_class {

Note that if you chain pattern databases, that is, use multiple databases in the same log path, the class assigned to the message (the value of ${.classifier.class}) will be the one assigned by the last pattern database. As a result, a message might be classified as unknown even if a previous parser successfully classified it. For example, consider the following configuration:

log {

Even if db_parser1 matches the message, db_parser2 might set ${.classifier.class} to unknown. To avoid this problem, you can use an 'if' statement to apply the second parser only if the first parser could not classify the message:

log {
    parser{ db-parser(file("db_parser1.xml")); };
    if (match("^unknown$" value(".classifier.class"))) {
        parser { db-parser(file("db_parser2.xml")); };

For details on how to create your own pattern databases see The syslog-ng pattern database format.

Using parser results in filters and templates

The results of message classification and parsing can be used in custom filters and templates, for example, in file and database templates. The following built-in macros allow you to use the results of the classification:

  • The .classifier.class macro contains the class assigned to the message (for example, violation, security, or unknown).

  • The .classifier.rule_id macro contains the identifier of the message pattern that matched the message.

  • The .classifier.context_id macro contains the identifier of the context for messages that were correlated. For details on correlating messages, see Correlating log messages using pattern databases.

Example: Using classification results for filtering messages

To filter on a specific message class, create a filter that checks the .classifier_class macro, and use this filter in a log statement.

filter fi_class_violation {
log {

Filtering on the unknown class selects messages that did not match any rule of the pattern database. Routing these messages into a separate file allows you to periodically review new or unknown messages.

To filter on messages matching a specific classification rule, create a filter that checks the .classifier.rule_id macro. The unique identifier of the rule (for example, e1e9c0d8-13bb-11de-8293-000c2922ed0a) is the id attribute of the rule in the XML database.

filter fi_class_rule {

Pattern database rules can assign tags to messages. These tags can be used to select tagged messages using the tags() filter function.

NOTE: The syslog-ng PE application automatically adds the class of the message as a tag using the .classifier.<message-class> format. For example, messages classified as "system" receive the .classifier.system tag. Use the tags() filter function to select messages of a specific class.

filter f_tag_filter {tags(".classifier.system");};

The message-segments parsed by the pattern parsers can also be used as macros as well. To accomplish this, you have to add a name to the parser, and then you can use this name as a macro that refers to the parsed value of the message.

Example: Using pattern parsers as macros

For example, you want to parse messages of an application that look like "Transaction: <type>.", where <type> is a string that has different values (for example, refused, accepted, incomplete, and so on). To parse these messages, you can use the following pattern:

'Transaction: @ESTRING::.@'

Here the @ESTRING@ parser parses the message until the next full stop character. To use the results in a filter or a filename template, include a name in the parser of the pattern, for example:


After that, add a custom template to the log path that uses this template. For example, to select every accepted transaction, use the following custom filter in the log path:

match("accepted" value("TRANSACTIONTYPE"));

NOTE: The above macros can be used in database columns and filename templates as well, if you create custom templates for the destination or logspace.

Use a consistent naming scheme for your macros, for example, APPLICATIONNAME_MACRONAME.

Downloading sample pattern databases

To simplify the building of pattern databases, One Identity has released (and will continue to release) sample databases. You can download sample pattern databases from the One Identity GitHub page (older samples are temporarily available here).

Note that these pattern databases are only samples and experimental databases. They are not officially supported, and may or may not work in your environment.

The syslog-ng pattern databases are available under the Creative Commons Attribution-Share Alike 3.0 (CC by-SA) license. This includes every pattern database written by community contributors or the One Identity staff. It means that:

  • You are free to use and modify the patterns for your needs.

  • If you redistribute the pattern databases, you must distribute your modifications under the same license.

  • If you redistribute the pattern databases, you must make it obvious that the source of the original syslog-ng pattern databases is the One Identity GitHub page.

For legal details, the full text of the license is available here.

If you create patterns that are not available in the GitHub repository, consider sharing them with us and the syslog-ng community. To do this, open a GitHub issue, or send them to the syslog-ng mailing list.

Correlating log messages using pattern databases

The syslog-ng PE application can correlate log messages identified using pattern databases. Alternatively, you can also correlate log messages using the grouping-by() parser. For details, see Correlating messages using the grouping-by() parser.

Log messages are supposed to describe events, but applications often separate information about a single event into different log messages. For example, the Postfix email server logs the sender and recipient addresses into separate log messages, or in case of an unsuccessful login attempt, the OpenSSH server sends a log message about the authentication failure, and the reason of the failure in the next message. Of course, messages that are not so directly related can be correlated as well, for example, login-logout messages, and so on.

To correlate log messages with syslog-ng PE, you can add messages into message-groups called contexts. A context consists of a series of log messages that are related to each other in some way, for example, the log messages of an SSH session can belong to the same context. As new messages come in, they may be added to a context. Also, when an incoming message is identified it can trigger actions to be performed, for example, generate a new message that contains all the important information that was stored previously in the context.

(For details on triggering actions and generating messages, see Triggering actions for identified messages.)

There are two attributes for pattern database rules that determine if a message matching the rule is added to a context: context-scope and context-id. The context-scope attribute acts as an early filter, selecting messages sent by the same process (${HOST}${PROGRAM}${PID} is identical), application (${HOST}${PROGRAM} is identical), or host, while the context-id actually adds the message to the context specified in the id. The context-id can be a simple string, or can contain macros or values extracted from the log messages for further filtering. If a message is added to a context, syslog-ng PE automatically adds the identifier of the context to the .classifier.context_id macro of the message.

NOTE: Message contexts are persistent and are not lost when syslog-ng PE is reloaded (SIGHUP), but are lost when syslog-ng PE is restarted.

Another parameter of a rule is the context-timeout attribute, which determines how long a context is stored, that is, how long syslog-ng PE waits for related messages to arrive.

Note the following points about timeout values:

  • When a new message is added to a context, syslog-ng PE will restart the timeout using the context-timeout set for the new message.

  • When calculating if the timeout has already expired or not, syslog-ng PE uses the timestamps of the incoming messages, not system time elapsed between receiving the two messages (unless the messages do not include a timestamp, or the keep-timestamp(no) option is set). That way syslog-ng PE can be used to process and correlate already existing log messages offline. However, the timestamps of the messages must be in chronological order (that is, a new message cannot be older than the one already processed), and if a message is newer than the current system time (that is, it seems to be coming from the future), syslog-ng PE will replace its timestamp with the current system time.

    Example: How syslog-ng PE calculates context-timeout

    Consider the following two messages:

    <38>1990-01-01T14:45:25 customhostname program6[1234]: program6 testmessage
    <38>1990-01-01T14:46:25 customhostname program6[1234]: program6 testmessage

    If the context-timeout is 10 seconds and syslog-ng PE receives the messages within 1 sec, the timeout event will occur immediately, because the difference of the two timestamps (60 sec) is larger than the timeout value (10 sec).

  • Avoid using unnecessarily long timeout values on high-traffic systems, as storing the contexts for many messages can require considerable memory. For example, if two related messages usually arrive within seconds, it is not needed to set the timeout to several hours.

Example: Using message correlation
<rule xml:id="..." context-id="ssh-session" context-timeout="86400" context-scope="process">
        <pattern>Accepted @ESTRING:usracct.authmethod: @for @ESTRING:usracct.username: @from @ESTRING:usracct.device: @port @ESTRING:: @@ANYSTRING:usracct.service@</pattern>

For details on configuring message correlation, see the context-id, context-timeout, and context-scope attributes of pattern database rules.

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