Package: drain3-ankcorn
Drain3Important UpdateDrain3 was moved to thelogpaiGitHub organization (which is also the home for the original Drain implementation). We always welcome more contributors and maintainers to join us and push the project forward. We welcome more contributions and variants of implementations if you find practical enhancements to the algorithm in production scenarios.IntroductionDrain3 is an online log template miner that can extract templates (clusters) from a stream of log messages in a timely
manner. It employs a parse tree with fixed depth to guide the log group search process, which effectively avoids
constructing a very deep and unbalanced tree.Drain3 continuously learns on-the-fly and extracts log templates from raw log entries.Example:For the input:connected to 10.0.0.1
connected to 192.168.0.1
Hex number 0xDEADBEAF
user davidoh logged in
user eranr logged inDrain3 extracts the following templates:ID=1     : size=2         : connected to <:IP:>
ID=2     : size=1         : Hex number <:HEX:>
ID=3     : size=2         : user <:*:> logged inFull sample program output:Starting Drain3 template miner
Checking for saved state
Saved state not found
Drain3 started with 'FILE' persistence
Starting training mode. Reading from std-in ('q' to finish)
> connected to 10.0.0.1
Saving state of 1 clusters with 1 messages, 528 bytes, reason: cluster_created (1)
{"change_type": "cluster_created", "cluster_id": 1, "cluster_size": 1, "template_mined": "connected to <:IP:>", "cluster_count": 1}
Parameters: [ExtractedParameter(value='10.0.0.1', mask_name='IP')]
> connected to 192.168.0.1
{"change_type": "none", "cluster_id": 1, "cluster_size": 2, "template_mined": "connected to <:IP:>", "cluster_count": 1}
Parameters: [ExtractedParameter(value='192.168.0.1', mask_name='IP')]
> Hex number 0xDEADBEAF
Saving state of 2 clusters with 3 messages, 584 bytes, reason: cluster_created (2)
{"change_type": "cluster_created", "cluster_id": 2, "cluster_size": 1, "template_mined": "Hex number <:HEX:>", "cluster_count": 2}
Parameters: [ExtractedParameter(value='0xDEADBEAF', mask_name='HEX')]
> user davidoh logged in
Saving state of 3 clusters with 4 messages, 648 bytes, reason: cluster_created (3)
{"change_type": "cluster_created", "cluster_id": 3, "cluster_size": 1, "template_mined": "user davidoh logged in", "cluster_count": 3}
Parameters: []
> user eranr logged in
Saving state of 3 clusters with 5 messages, 644 bytes, reason: cluster_template_changed (3)
{"change_type": "cluster_template_changed", "cluster_id": 3, "cluster_size": 2, "template_mined": "user <:*:> logged in", "cluster_count": 3}
Parameters: [ExtractedParameter(value='eranr', mask_name='*')]
> q
Training done. Mined clusters:
ID=1     : size=2         : connected to <:IP:>
ID=2     : size=1         : Hex number <:HEX:>
ID=3     : size=2         : user <:*:> logged inThis project is an upgrade of the originalDrainproject by LogPAI from Python 2.7 to Python 3.6 or later with additional features and bug-fixes.Read more information about Drain from the following paper:Pinjia He, Jieming Zhu, Zibin Zheng, and Michael R.
Lyu.Drain: An Online Log Parsing Approach with Fixed Depth Tree,
Proceedings of the 24th International Conference on Web Services (ICWS), 2017.A Drain3 use case is presented in this blog
post:Use open source Drain3 log-template mining project to monitor for network outages.New featuresPersistence. Save and load Drain state into anApache Kafkatopic,Redisor a file.Streaming. Support feeding Drain with messages one-be-one.Masking. Replace some message parts (e.g numbers, IPs, emails) with wildcards. This improves the
accuracy of template mining.Packaging. As a pip package.Configuration. Support for configuring Drain3 using an.inifile or a configuration object.Memory efficiency. Decrease the memory footprint of internal data structures and introduce
cache to control max memory consumed (thanks to @StanislawSwierc)Inference mode. In case you want to separate training and inference phase, Drain3
provides a function forfastmatching against already-learned clusters (templates) only, without the usage of
regular expressions.Parameter extraction. Accurate extraction of the variable parts from a log message as an
ordered list, based on its mined template and the defined masking instructions (thanks to @Impelon).Expected Input and OutputAlthough Drain3 can be ingested with full raw log message, template mining accuracy can be improved if you feed it with
only the unstructured free-text portion of log messages, by first removing structured parts like timestamp, hostname.
severity, etc.The output is a dictionary with the following fields:change_type- indicates either if a new template was identified, an existing template was changed or message added
to an existing cluster.cluster_id- Sequential ID of the cluster that the log belongs to.cluster_size- The size (message count) of the cluster that the log belongs to.cluster_count- Count clusters seen so far.template_mined- the last template of above cluster_id.ConfigurationDrain3 is configured usingconfigparser. By default, config
filename isdrain3.iniin working directory. It can also be configured passing
aTemplateMinerConfigobject to theTemplateMinerconstructor.Primary configuration parameters:[DRAIN]/sim_th- similarity threshold. if percentage of similar tokens for a log message is below this number, a new
log cluster will be created (default 0.4)[DRAIN]/depth- max depth levels of log clusters. Minimum is 3. (default 4)[DRAIN]/max_children- max number of children of an internal node (default 100)[DRAIN]/max_clusters- max number of tracked clusters (unlimited by default). When this number is reached, model
starts replacing old clusters with a new ones according to the LRU cache eviction policy.[DRAIN]/extra_delimiters- delimiters to apply when splitting log message into words (in addition to whitespace) (
default none). Format is a Python list e.g.['_', ':'].[MASKING]/masking- parameters masking - in json format (default "")[MASKING]/mask_prefix&[MASKING]/mask_suffix- the wrapping of identified parameters in templates. By default, it
is<and>respectively.[SNAPSHOT]/snapshot_interval_minutes- time interval for new snapshots (default 1)[SNAPSHOT]/compress_state- whether to compress the state before saving it. This can be useful when using Kafka
persistence.MaskingThis feature allows masking of specific variable parts in log message with keywords, prior to passing to Drain. A
well-defined masking can improve template mining accuracy.Template parameters that do not match any custom mask in the preliminary masking phase are replaced with<*>by Drain
core.Use a list of regular expressions in the configuration file with the format{'regex_pattern', 'mask_with'}to set
custom masking.For example, following masking instructions indrain3.iniwill mask IP addresses and integers:[MASKING]
masking = [
          {"regex_pattern":"((?<=[^A-Za-z0-9])|^)(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})((?=[^A-Za-z0-9])|$)", "mask_with": "IP"},
          {"regex_pattern":"((?<=[^A-Za-z0-9])|^)([\\-\\+]?\\d+)((?=[^A-Za-z0-9])|$)", "mask_with": "NUM"},
          ]
    ]PersistenceThe persistence feature saves and loads a snapshot of Drain3 state in a (compressed) json format. This feature adds
restart resiliency to Drain allowing continuation of activity and maintain learned knowledge across restarts.Drain3 state includes the search tree and all the clusters that were identified up until snapshot time.The snapshot also persist number of log messages matched each cluster, and it'scluster_id.An example of a snapshot:{"clusters":[{"cluster_id":1,"log_template_tokens":["aa","aa","<*>"],"py/object":"drain3_core.LogCluster","size":2},{"cluster_id":2,"log_template_tokens":["My","IP","is","<IP>"],"py/object":"drain3_core.LogCluster","size":1}]}This example snapshot persist two clusters with the templates:["aa", "aa", "<*>"]- occurs twice["My", "IP", "is", "<IP>"]- occurs onceSnapshots are created in the following events:cluster_created- in any new templatecluster_template_changed- in any update of a templateperiodic- after n minutes from the last snapshot. This is intended to save cluster sizes even if no new template
was identified.Drain3 currently supports the following persistence modes:Kafka- The snapshot is saved in a dedicated topic used only for snapshots - the last message in this topic is the
last snapshot that will be loaded after restart. For Kafka persistence, you need to provide:topic_name. You may
also provide otherkwargsthat are supported bykafka.KafkaConsumerandkafka.Producere.gbootstrap_serversto change Kafka endpoint (default islocalhost:9092).Redis- The snapshot is saved to a key in Redis database (contributed by @matabares).File- The snapshot is saved to a file.Memory- The snapshot is saved an in-memory object.None- No persistence.Drain3 persistence modes can be easily extended to another medium / database by inheriting
thePersistenceHandlerclass.Training vs. Inference modesIn some use-cases, it is required to separate training and inference phases.In training phase you should calltemplate_miner.add_log_message(log_line). This will match log line against an
existing cluster (if similarity is above threshold) or create a new cluster. It may also change the template of an
existing cluster.In inference mode you should calltemplate_miner.match(log_line). This will match log line against previously learned
clusters only. No new clusters are created and templates of existing clusters are not changed. Match to existing cluster
has to be perfect, otherwiseNoneis returned. You can use persistence option to load previously trained clusters
before inference.Memory efficiencyThis feature limits the max memory used by the model. It is particularly important for large and possibly unbounded log
streams. This feature is controlled by themax_clusters​parameter, which sets the max number of clusters/templates
trarcked by the model. When the limit is reached, new templates start to replace the old ones according to the Least
Recently Used (LRU) eviction policy. This makes the model adapt quickly to the most recent templates in the log stream.Parameter ExtractionDrain3 supports retrieving an ordered list of variables in a log message, after its template was mined. Each parameter
is accompanied by the name of the mask that was matched, or*for the catch-all mask.Parameter extraction is performed by generating a regular expression that matches the template and then applying it on
the log message. Whenexact_matchingis enabled (by default), the generated regex included the regular expression
defined in relevant masking instructions. If there are multiple masking instructions with the same name, either match
can satisfy the regex. It is possible to disable exact matching so that every variable is matched against a
non-whitespace character sequence. This may improve performance on expanse of accuracy.Parameter extraction regexes generated per template are cached by default, to improve performance. You can control cache
size with theMASKING/parameter_extraction_cache_capacityconfiguration parameter.Sample usage:result=template_miner.add_log_message(log_line)params=template_miner.extract_parameters(result["template_mined"],log_line,exact_matching=True)For the input"user johndoe logged in 11 minuts ago", the template would be:"user <:*:> logged in <:NUM:> minuts ago"... and the extracted parameters:[
  ExtractedParameter(value='johndoe', mask_name='*'), 
  ExtractedParameter(value='11', mask_name='NUM')
]InstallationDrain3 is available fromPyPI. To install usepip:pip3 install drain3Note: If you decide to use Kafka or Redis persistence, you should install relevant client library explicitly, since it
is declared as an extra (optional) dependency, by either:pip3 install kafka-python-- or --pip3 install redisExamplesIn order to run the examples directly from the repository, you need to install dependencies. You can do that using *
pipenv* by executing the following command (assuming pipenv already installed):python3-mpipenvsyncExample 1 -drain_stdin_demoRunexamples/drain_stdin_demo.pyfrom the root folder of the repository by:python3 -m pipenv run python -m examples.drain_stdin_demoThis example uses Drain3 on input from stdin and persist to either Kafka / file / no persistence.Changepersistence_typevariable in the example to change persistence mode.Enter several log lines using the command line. Pressqto end online learn-and-match mode.Next, demo goes to match (inference) only mode, in which no new clusters are trained and input is matched against
previously trained clusters only. Pressqagain to finish execution.Example 2 -drain_bigfile_demoRunexamples/drain_bigfile_demofrom the root folder of the repository by:python3 -m pipenv run python -m examples.drain_bigfile_demoThis example downloads a real-world log file (of an SSH server) and process all lines, then prints result clusters,
prefix tree and performance statistics.Sample config fileAn exampledrain3.inifile with masking instructions can be found in theexamplesfolder as well.ContributingOur project welcomes external contributions. Please refer toCONTRIBUTING.mdfor further details.Change Logv0.9.11Fixed possible DivideByZero error when the profiler is enabled -Issue #65.v0.9.10Fixed compatibility issue with Python 3.10 caused by removal ofKeysView.v0.9.9Added support for accurate log message parameter extraction in a new function -extract_parameters(). The
functionget_parameter_list()is deprecated (Thanks to@Impelon).RefactoredAbstractMaskingInstructionas a base class forRegexMaskingInstruction, allowing to introduce other
types of masking mechanisms.v0.9.8Added an optionfull_search_strategyoption inTemplateMiner.match()andDrain.match(). See more info at
Issue#48.Added an option to disable parameterization of tokens that contains digits in
configuration:TemplateMinerConfig.parametrize_numeric_tokensLoading Drain snapshot now only restores clusters state and not configuration parameters. This improves backwards
compatibility when introducing new Drain configuration parameters.v0.9.7Fixed bug in original Drain: log clusters were created multiple times for log messages with fewer tokens
thanmax_node_depth.Changeddepthproperty name to a more descriptive namemax_node_depthas Drain always subtracts 2 ofdepthargument value. Also addedlog_cluster_depthproperty to reflect original value of depth argument (Breaking Change).Restricteddepthparam to minimum sensible value of 3.Added log cluster count to nodes inDrain.print_tree()Added optional log cluster details toDrain.print_tree()v0.9.6Fix issuehttps://github.com/IBM/Drain3/issues/38: Unnecessary update of LRU cache in casemax_clustersis used (
thanks@StanislawSwierc).v0.9.5Added:TemplateMiner.match()function for fast matching against existing clusters only.v0.9.4Added:TemplateMiner.get_parameter_list()function to extract template parameters for raw log message (thanks to *
@cwyalpha*)Added option to customize mask wrapper - Instead of the default<*>,<NUM>etc, you can select any wrapper prefix
or suffix by overridingTemplateMinerConfig.mask_prefixandTemplateMinerConfig.mask_prefixFixed: config.inifile is always read from same folder as source file in demos in tests (thanks@RobinMaas95)v0.9.3Fixed: comparison of type int with type str in functionadd_seq_to_prefix_tree#28 (bug introduced at v0.9.1)v0.9.2Updated jsonpickle versionKeysid_to_clusterdict are now persisted by jsonpickle asintinstead ofstrto avoid keys type conversion on
load snapshot which caused some issues.Added cachetools dependency tosetup.py.v0.9.1Added option to configureTemplateMinerusing a configuration object (without.inifile).Support forprint_tree()to a file/stream.AddedMemoryBufferPersistenceAdded unit tests for state save/load.Bug fix: missing type-conversion in state loading, introduced in v0.9.0Refactor: Drain prefix tree keys are now of typestralso for 1st level
(wasintbefore), for type consistency.v0.9.0Decrease memory footprint of the main data structures.Addedmax_clustersoption to limit the number of tracked clusters.Changed cluster identifier type from str to intAdded more unit tests and CIv0.8.6Addedextra_delimitersconfiguration option to Drainv0.8.5Profiler improvementsv0.8.4Masking speed improvementv0.8.3Fix: profiler state after load from snapshotv0.8.2Fixed snapshot backward compatibility to v0.7.9v0.8.1Bugfix in profiling configuration readv0.8.0Added time profiling support (disabled by default)Added cluster ID to snapshot reason log (credit: @boernd)Minor Readability and documentation improvements in Drainv0.7.9Fix:KafkaPersistencenow accepts alsobootstrap_serversas kwargs.v0.7.8Usingkafka-pythonpackage instead ofkafka(newer).Added support for specifying additional configuration askwargsin Kafka persistence handler.v0.7.7Corrected default Drain config values.v0.7.6Improvement in config file handling (Note: new sections were added instead ofDEFAULTsection)v0.7.5Made Kafka and Redis optional requirements