Please find the changelog for VictoriaMetrics Anomaly Detection below.
v1.18.8 #
Released: 2024-12-03
IMPROVEMENT: Added a
scale
parameter toProphetModel
. It allows for proportional scaling of the confidence intervals generated byinterval_width
. If set > 1, it may help reducing false positives in scenarios where the data contains many sharp but expected seasonal peaks that may not be well captured by Prophet’s seasonal Fourier terms.FIX: Corrected an issue in
ProphetModel
when using tz-aware mode withtz_seasonalities
includingdow
(day of the week). Previously, Sundays were incorrectly handled due to a mismatch between the weekday indices. This caused Sundays to lack weekly seasonality features, defaulting to just averaged trends.
v1.18.7 #
Released: 2024-12-02
IMPROVEMENT: Introduced a new
push_frequency
parameter for the monitoring.push component, with a default value of 15m. This enhancement ensures better alignment with pull-based monitoring behavior and improves self-monitoring experience ofvmanomaly
in setups with infrequent schedules (e.g., rarefit_every
orinfer_every
intervals) to deal with data staleness.FIX: Fixed a bug, introduced in v1.18.5, that prevented the monitoring.push component from properly instantiating and pushing self-monitoring metrics.
v1.18.6 #
Released: 2024-12-01
Note: Release v1.18.5 contained an issue that prevented the monitoring.push component from properly instantiating and pushing self-monitoring metrics. This issue has been resolved in patch v1.18.7, please update to apply the fix.
- FIX: Assure proper validation of BacktestingScheduler arguments, if specified in ISO-8601 format, preventing service crashes due to validation errors.
v1.18.5 #
Released: 2024-11-27
Note: This release contained an issue that prevented the monitoring.push component from properly instantiating and pushing self-monitoring metrics. This issue has been resolved in patch v1.18.7, please update to apply the fix.
- IMPROVEMENT: Introduced the ability to run
vmanomaly
using a configuration directory. This enhancement allows users to recursively merge multiple full configuration files (previously limited to merging specific sections, such asreader
) and execute a single instance of the service with the combined configuration. - IMPROVEMENT: Added a new utility,
config_splitter.py
, to streamline the process of splitting a single configuration file into multiple standalone configurations. The configurations are split by specified entities likeschedulers
,models
,queries
orextra_filters
. The split configurations can be saved to a designated directory. It simplifies scalingvmanomaly
and enhances user experience by automating the process of separating config files so they can be run on separate instances of vmanomaly. For more details, refer to this section. - IMPROVEMENT: Introduced the ability to configure the
PeriodicScheduler
to start at a specific time using thestart_from
andtz
parameters. Thestart_from
parameter accepts eitherHH:MM
or ISO 8601 formats, withtz
defaulting toUTC
. Ifstart_from
is in the past, the next valid start time is automatically calculated based on thefit_every
interval.
v1.18.4 #
Released: 2024-11-18
- IMPROVEMENT: Introduced self-monitoring guide for
vmanomaly
. Added metrics for total RAMvmanomaly_available_memory_bytes
and the number of logical CPU coresvmanomaly_cpu_cores_available
to the self-monitoring metrics.
v1.18.3 #
Released: 2024-11-14
- FIX: This patch release resolves an issue that could cause a service crash when parallelizing data processing with
VmReader
. Affected releases: v1.18.1 - v1.18.2.
v1.18.2 #
Released: 2024-11-13
Note: In release v1.18.1, an issue was identified that could lead to a service crash during parallelized data processing with VmReader. Please update to patch v1.18.3, which resolves this issue.
- IMPROVEMENT: Enhanced the flexibility of the
ProphetModel
for tz-aware data (tz_aware = True
). Thetz_seasonalities
argument has been reformatted to align with the structure of the existingseasonalities
argument. For more details, refer to the model section here. Additionally, tz-aware support forProphetModel
has been added toAutoTuned
model wrapper. This feature is automatically enabled if the data is timezone-aware and its timezone is not set to the default (‘UTC’), otherwise default timezone-free optimization flow will be used.
v1.18.1 #
Released: 2024-11-12
Note: In release v1.18.1, an issue was identified that could lead to a service crash during parallelized data processing with VmReader. Please update to patch v1.18.3, which resolves this issue.
IMPROVEMENT: Added a reader-level
data_range
argument, allowing users to define a default valid data range for all input queries inqueries
. Individual queries can still override this default with their owndata_range
if needed.IMPROVEMENT: Added the
url
label to enhance labelset consistency across self-monitoring metrics in both reader and writer components. Metrics affected:vmanomaly_reader_received_bytes
vmanomaly_reader_response_parsing_seconds
vmanomaly_reader_timeseries_received
vmanomaly_reader_datapoints_received
vmanomaly_writer_request_serialize_seconds
vmanomaly_writer_datapoints_sent
vmanomaly_writer_timeseries_sent
FIX: Resolved an issue where rolling models incorrectly set their last seen
infer
timestamp during firstfit_infer
call, resulting in output being produced for every datapoint within thefit_window
on its first invocation.FIX: Resolved an issue in multi-scheduler configurations where self-monitoring metric values were overwriting each other.
FIX: Resolved an issue causing incorrect
query_key
label values in thevmanomaly_model_datapoints_produced
self-monitoring metric for univariate models.FIX: Resolved an issue that caused the
vmanomaly_model_runs
self-monitoring metric to miss increments for rolling models.FIX: Aligned the calculations of
vmanomaly_model_datapoints_accepted
andvmanomaly_model_datapoints_produced
self-monitoring model metrics across all stages (fit
,infer
, andfit_infer
) for consistency.
v1.18.0 #
Released: 2024-10-28
FEATURE: Introduced timezone-aware support in
VmReader
for accurate seasonality modeling, especially during DST shifts. A newtz
argument enables timezone offset management at both global and query-specific levels.- Enhanced
ProphetModel
with atz_aware
argument (combined withtz_seasonalities
andtz_use_cyclical_encoding
) for timezone-aware timestamps. This addresses a limitation in Prophet’s native design that doesn’t allow timezone-aware and DST-aware seasonality.
- Enhanced
IMPROVEMENT: Enhanced error handling in VmReader to provide clearer diagnostics and broader coverage.
FIX: Updated
vmanomaly_version_info
andvmanomaly_ui_version_info
gauges to correctly set the version label value based on image tags.FIX: The
n_samples_seen_
attribute now properly resets to 0 with each newfit
call in online model classes (OnlineMADModel
andOnlineQuantileModel
), ensuring accurate tracking of processed sample count.
v1.17.2 #
Released: 2024-10-22
- IMPROVEMENT: Added
vmanomaly_version_info
(service) andvmanomaly_ui_version_info
(vmui) gauges to self-monitoring metrics. - IMPROVEMENT: Added
instance
andjob
labels to pushed metrics so they have the same labels as vmanomaly metrics that are pulled/scraped. Metric labels can be customized via theextra_labels
argument. By default job label will bevmanomaly
and the instance label will bef'{hostname}:{vmanomaly_port}
. See monitoring.push for examples and details. - IMPROVEMENT: Added a subsection to monitoring page with detailed per-component service logs, including reader and writer logs, error handling, metrics updates, and multi-tenancy warnings.
- IMPROVEMENT: Added a new Command-line arguments subsection to the Quickstart guide, providing details on available options for configuring
vmanomaly
.
v1.17.1 #
Released: 2024-10-18
- FIX: Prophet models no longer fail to train on constant data, data consisting of the same value and no variation across time. The bug prevented the
fit
stage from completing successfully, resulting in the model instance not being stored in the model registry, after automated model cleanup was added in v1.17.0.
v1.17.0 #
Released: 2024-10-17
FEATURE: Added
max_points_per_query
(global and query-specific) VmReader arg to control query chunking. This overrides howsearch.maxPointsPerTimeseries
flag (introduced in v1.14.1) is used invmanomaly
for splitting longfit_window
queries into smaller sub-intervals. This helps users avoid hitting thesearch.maxQueryDuration
limit for individual queries by distributing initial query across multiple subquery requests with minimal overhead.IMPROVEMENT: Enhanced the self-monitoring metrics for consistency across the components. Key changes include:
- Converted several self-monitoring metrics from
Summary
toHistogram
to enable quantile calculation. This addresses the limitation of theprometheus_client
’s Summary implementation, which does not support quantiles. The change ensures metrics are more informative for performance analysis. Affected metrics are: - Added a
query_key
label to thevmanomaly_reader_response_parsing_seconds
metric to provide finer granularity in tracking the performance of individual queries. This metric has also been switched fromSummary
toHistogram
to align with the other metrics and support quantile calculations. - Added
preset
andscheduler_alias
keys to VmReader and VmWriter metrics for consistency in multi-scheduler setups. - Renamed Counters
vmanomaly_reader_response_count
tovmanomaly_reader_responses
andvmanomaly_writer_response_count
tovmanomaly_writer_responses
. - Updated docs for better clarity.
- Converted several self-monitoring metrics from
IMPROVEMENT: Accelerated performance of model fitting stages on multicore systems.
IMPROVEMENT: Optimized query handling in multi-scheduler setups by filtering queries for each scheduler based on model requirements. This reduces unnecessary data fetching from VictoriaMetrics, ensuring only relevant queries are processed by the VmReader, leading to better performance and efficiency of configs with multiple active schedulers.
IMPROVEMENT: Implemented automatic cleanup of files in subdirectories within
/tmp
(generated by the Stan backend when utilizing Prophet models) after eachfit
operation. This prevents the accumulation of unused data over time in/tmp
, addressing a potential issue where these files would only be deleted upon termination of the current Python session or service, leading to uncontrolled disk growth.FIX: Re-enable the
vmanomaly_reader_response_count
(now calledvmanomaly_reader_responses
) self-monitoring metric for the VmReader, which was unintentionally disabled in previous releases and now updates correctly as intended.
v1.16.3 #
Released: 2024-10-08
- IMPROVEMENT: Added
tls_cert_file
andtls_key_file
arguments to support mTLS (mutual TLS) invmanomaly
components. This enhancement applies to the following components: VmReader, VmWriter, and Monitoring/Push. You can also use these arguments in conjunction withverify_tls
when it is set as a path to a custom CA certificate file.
v1.16.2 #
Released: 2024-10-06
FEATURE: Added support for
multitenant
value intenant_id
arg to enable querying across multiple tenants in VictoriaMetrics cluster (option available from v1.104.0):FIX: Resolved an issue with handling an empty
preset
value (e.g.,preset: ""
) that was preventing the default helm chart from being deployed.
v1.16.1 #
Released: 2024-10-02
- FIX: This patch release prevents the service from crashing by rolling back the version of a third-party dependency. Affected releases: v1.16.0.
v1.16.0 #
Released: 2024-10-01
Note: A bug was discovered in this release that causes the service to crash. Please use the patch v1.16.1 to resolve this issue.
FEATURE: Introduced data dumps to a host filesystem for VmReader. Resource-intensive setups (multiple queries returning many metrics, bigger
fit_window
arg) will have RAM consumption reduced during fit calls.IMPROVEMENT: Added a
groupby
argument for logical grouping in multivariate models. When specified, a separate multivariate model is trained for each unique combination of label values in thegroupby
columns. For example, to perform multivariate anomaly detection on metrics at the machine level without cross-entity interference, you can usegroupby: [host]
orgroupby: [instance]
, ensuring one model per entity being trained (e.g., per host). Please find more details here.IMPROVEMENT: Improved performance of VmReader on multicore instances for reading and data processing.
IMPROVEMENT: Introduced new CLI argument aliases to enhance compatibility with Helm charts (i.e. using secrets) and better align with VictoriaMetrics flags:
--licenseFile
as an alias for--license-file
--license.forceOffline
as an alias for--license-verify-offline
--loggerLevel
as an alias for--log-level
- The previous argument format is retained for backward compatibility.
FIX: The
provide_series
common argument now correctly filters the written time series in the IsolationForestMultivariate model.
v1.15.9 #
Released: 2024-08-27
- IMPROVEMENT: Added support for bearer token authentication in
push
mode within the self-monitoring configuration section.
v1.15.8 #
Released: 2024-08-27
- FIX: Made minor adjustments to how the reader and writer handle bearer tokens across different modes.
v1.15.7 #
Released: 2024-08-27
- FIX: Made minor adjustments to how the reader and writer handle bearer tokens across different modes.
v1.15.6 #
Released: 2024-08-26
- IMPROVEMENT: Introduced the
bearer_token_file
argument to the reader and writer components to enhance secret management.
v1.15.5 #
Released: 2024-08-19
- FIX: following v1.15.2 online model enhancement, now
data_range
parameter is correctly initialized for online models, created (for new time series returned by particular query) duringinfer
calls.
v1.15.4 #
Released: 2024-08-15
- IMPROVEMENT: better config handling of writer and monitoring sections if using
vmanomaly
with helm charts.
v1.15.3 #
Released: 2024-08-14
- IMPROVEMENT: better config handling of
reader
section if usingvmanomaly
with helm charts.
v1.15.2 #
Released: 2024-08-13
- IMPROVEMENT: Enhanced online models (e.g.,
OnlineQuantileModel
) to automatically create model instances for unseen time series duringinfer
calls, eliminating the need to wait for the nextfit
call. This ensures no inferences are skipped when using online models. - FIX: Corrected an issue with the
OnlineMADModel
to ensure proper functionality when used in combination with on-disk model dump mode. - FIX: Addressed numerical instability in the
OnlineQuantileModel
whenuse_transform
is set toTrue
. - FIX: Resolved a logging issue that could cause a
RuntimeError: reentrant call inside <_io.BufferedWriter name='<stderr>'>
when a termination event was received.
v1.15.1 #
Released: 2024-08-10
FEATURE: Introduced backward-compatible
data_range
query-specific parameter to the VmReader. It enables the definition of valid data ranges for input per individual query inqueries
, resulting in:- High anomaly scores (>1) when the data falls outside the expected range, indicating a data constraint violation.
- Lowest anomaly scores (=0) when the model’s predictions (
yhat
) fall outside the expected range, signaling uncertain predictions. - For more details, please refer to the documentation.
IMPROVEMENT: Added
latency_offset
argument to the VmReader to override the default-search.latencyOffset
flag of VictoriaMetrics (30s). The default value is set to 1ms, which should help in cases wheresampling_frequency
is low (10-60s) andsampling_frequency
equalsinfer_every
in the PeriodicScheduler. This prevents users from receivingservice - WARNING - [Scheduler [scheduler_alias]] No data available for inference.
warnings in logs and allows for consecutiveinfer
calls without gaps. To restore the backward compatible behavior, set it equal to your-search.latencyOffset
value in VmReader config section.FIX: Ensure the
use_transform
argument of theOnlineQuantileModel
functions as intended.FIX: Add a docstring for
query_from_last_seen_timestamp
arg of VmReader.
v1.15.0 #
Released: 2024-08-06
FEATURE: Introduced models that support online learning for stream-like input. These models significantly reduce the amount of data required for the initial fit stage. For example, they enable reducing
fit_every
from weeks to hours and increasingfit_every
from hours to weeks in the PeriodicScheduler, significantly reducing the peak amount of data queried from VictoriaMetrics duringfit
stages. The next models were added:OnlineZscoreModel
- online version of existing Z-score implementation with the same exact behavior.OnlineMADModel
- online version of existing MADModel implementation with approximate behavior, based on t-digests for online quantile estimation.OnlineQuantileModel
- online quantile model, that supports custom ranges for seasonality estimation to cover more complex data patterns.- Find out more about online models specifics in correspondent section.
FEATURE: Introduced the
optimized_business_params
key (list of strings) to theAutoTuned
optimization_params
. This allows particular business-specific parameters such asdetection_direction
andmin_dev_from_expected
to remain unchanged during optimizations, retaining their default values.IMPROVEMENT: Optimized the
AutoTuned
model logic to minimize deviations from the expectedanomaly_percentage
specified in the configuration and the detected percentage in the data, while also reducing discrepancies between the actual values (y
) and the predictions (yhat
).IMPROVEMENT: Allow
ProphetModel
to fit with multiple seasonalities when used inAutoTuned
mode.
v1.14.2 #
Released: 2024-07-26
v1.14.1 #
Released: 2024-07-26
FEATURE: Allow to process larger data chunks in VmReader that exceed
-search.maxPointsPerTimeseries
constraint in VictoriaMetrics by splitting the range and sending multiple requests. A warning is printed in logs, suggesting reducing the range or step, or increasingsearch.maxPointsPerTimeseries
constraint in VictoriaMetrics, which is still a recommended option.FEATURE: Backward-compatible redesign of
queries
arg of VmReader. Old format of{q_alias1: q_expr1, q_alias2: q_expr2, ...}
will be implicitly converted to a new one with a warning raised in logs. New format allows to specify per-query parameters, likestep
to reduce amount of data read from VictoriaMetrics TSDB and to allow config flexibility. Find out more in Per-query parameters section of VmReader.IMPROVEMENT: Added multi-platform builds for
linux/amd64
andlinux/arm64
architectures.
v1.13.3 #
Released: 2024-07-17
- FIX: now validation of
args
argument forHoltWinters
model works properly.
v1.13.2 #
Released: 2024-07-15
- IMPROVEMENT: update
node-exporter
preset to reduce false positives - FIX: add
verify_tls
arg forpush
monitoring section. Also,verify_tls
is now correctly used in VmWriter. - FIX: now
AutoTuned
model wrapper works correctly in on-disk model storage mode. - FIX: now rolling models, like
RollingQuantile
are properly handled in One-off scheduler, when wrapped inAutoTuned
v1.13.0 #
Released: 2024-06-11
- FEATURE: Introduced
preset
mode to run vmanomaly service with minimal user input and on widely-known metrics, like those produced bynode_exporter
. - FEATURE: Introduced
min_dev_from_expected
model common arg, aimed at reducing false positives in scenarios where deviations between the real valuey
and the expected valueyhat
are relatively high and may cause models to generate high anomaly scores. However, these deviations are not significant enough in absolute values to be considered anomalies based on domain knowledge. - FEATURE: Introduced
detection_direction
model common arg, enabling domain-driven anomaly detection strategies. Configure models to identify anomalies occurring above, below, or in both directions relative to the expected values. - FEATURE: add
n_jobs
arg toBacktestingScheduler
to allow proportionally faster (yet more resource-intensive) evaluations of a config on historical data. Default value is 1, that implies sequential execution. - FEATURE: allow anomaly detection models to be dumped to a host filesystem after
fit
stage (instead of in-memory). Resource-intensive setups (many models, many metrics, biggerfit_window
arg) and/or 3rd-party models that store fit data (like ProphetModel or HoltWinters) will have RAM consumption greatly reduced at a cost of slightly slowerinfer
stage. Please find how to enable it here - IMPROVEMENT: Reduced the resource used for each fitted
ProphetModel
by up to 6 times. This includes both RAM for in-memory models and disk space for on-disk models storage. For more details, refer to this discussion on Facebook’s Prophet. - IMPROVEMENT: now config components class can be referenced by a short alias instead of a full class path - i.e.
model.zscore.ZscoreModel
becomeszscore
,reader.vm.VmReader
becomesvm
,scheduler.periodic.PeriodicScheduler
becomesperiodic
, etc. - FIX: if using multi-scheduler setup (introduced in v1.11.0), prevent schedulers (and correspondent services) that are not attached to any model (so neither found in ‘schedulers’ arg nor left blank in
model
section) from being spawn, causing resource overhead and slight interference with existing ones. - FIX: set random seed for ProphetModel to assure uncertainty estimates (like
yhat_lower
,yhat_upper
) and dependant series (likeanomaly_score
), produced during.infer()
calls are always deterministic given the same input. See initial issue for the details. - FIX: prevent orphan queries (that are not attached to any model or scheduler) found in
queries
arg of Reader config section to be fetched from VictoriaMetrics TSDB, avoiding redundant data processing. A warning will be logged, if such queries exist in a parsed config.
v1.12.0 #
Released: 2024-03-31
- FEATURE: Introduction of
AutoTunedModel
model class to optimize any built-in model on data duringfit
phase. Specify as little asanomaly_percentage
param from(0, 0.5)
interval andtuned_model_class
(i.e.model.zscore.ZscoreModel
) to get it working with best settings that match your data. See details here.
- IMPROVEMENT: Better logging of model lifecycle (fit/infer stages).
- IMPROVEMENT: Introduce
provide_series
arg to all the built-in models to define what output fields to generate for writing (i.e.provide_series: ['anomaly_score']
means only scores are being produced) - FIX: Self-monitoring metrics are now aggregated to
queries
aliases level (not to label sets of individual timeseries) and aligned with reader, writer and model sections description , so/metrics
endpoint holds only necessary information for scraping. - FIX: Self-monitoring metric
vmanomaly_models_active
now has additional labelsmodel_alias
,scheduler_alias
,preset
to align with model-centric self-monitoring. - IMPROVEMENT: Add possibility to use temporal information in IsolationForest models via cyclical encoding. This is particularly helpful to detect multivariate seasonality-dependant anomalies.
- BREAKING CHANGE: ARIMA model is removed from built-in models. For affected users, it is suggested to replace ARIMA by Prophet or Holt-Winters.
v1.11.0 #
Released: 2024-02-22
- FEATURE: Multi-scheduler support. Now users can use multiple model specs in a single config (via aliasing), each spec can be run with its own (even multiple) schedulers.
- Introduction of
schedulers
arg in model spec:- It allows each model to be managed by 1 (or more) schedulers, so overall resource usage is optimized and flexibility is preserved.
- Passing an empty list or not specifying this param implies that each model is run in all the schedulers, which is a backward-compatible behavior.
- Please find more details in docs on Model section
- Introduction of
- DEPRECATION: slight refactor of a scheduler config section
- DEPRECATION: The
--watch
CLI option for config file reloads is deprecated and will be ignored in the future.
v1.10.0 #
Released: 2024-02-15
FEATURE: Multi-model support. Now users can specify multiple model specs in a single config (via aliasing), as well as to reference what queries from VmReader it should be run on.
- Introduction of
queries
arg in model spec:- It allows the model to be executed only on a particular query subset from
reader
section. - Passing an empty list or not specifying this param implies that each model is run on results from all queries, which is a backward-compatible behavior.
- Please find more details in docs on Model section
- It allows the model to be executed only on a particular query subset from
- Introduction of
DEPRECATION: slight refactor of a model config section
- Now models are passed as a mapping of
model_alias: model_spec
under model sections. Using old format (<= 1.9.2) will produce warnings for now and will be removed in future versions. - Please find more details in docs on Model section
- Now models are passed as a mapping of
IMPROVEMENT: now logs from
monitoring.pull
GET requests to/metrics
endpoint are shown only in DEBUG modeIMPROVEMENT: labelset for multivariate models is deduplicated and cleaned, resulting in better UX
Note: These updates support more flexible setup and effective resource management in service, as now it’s not longer needed to spawn several instances of
vmanomaly
to split queries/models context across.
v1.9.2 #
Released: 2024-01-29
- BUGFIX: now multivariate models (like
IsolationForestMultivariateModel
) are properly handled throughout fit/infer phases.
v1.9.1 #
Released: 2024-01-27
- IMPROVEMENT: Updated the offline license verification backbone to mitigate a critical vulnerability identified in the
ecdsa
library, ensuring enhanced security despite initial non-impact. - IMPROVEMENT: bump 3rd-party dependencies for Python 3.12.1
v1.9.0 #
Released: 2024-01-26
- BUGFIX: The
query_from_last_seen_timestamp
internal logic in VmReader, first introduced in v1.5.1, now functions correctly. This fix ensures that the input data shape remains consistent for subsequentfit
-based model calls in the service. - BREAKING CHANGE: The
sampling_period
parameter is now mandatory in VmReader. This change aims to clarify and standardize the frequency of input/output invmanomaly
, thereby reducing uncertainty and aligning with user expectations.
Note: The majority of users, who have been proactively specifying the
sampling_period
parameter in their configurations, will experience no disruption from this update. This transition formalizes a practice that was already prevalent and expected among our user base.
v1.8.0 #
Released: 2024-01-15
- FEATURE: Added Univariate MAD (median absolute deviation) model support.
- IMPROVEMENT: Update Python to 3.12.1 and all the dependencies.
- IMPROVEMENT: Don’t check /health endpoint, check the real /query_range or /import endpoints directly. Users kept getting problems with /health.
- DEPRECATION: “health_path” param is deprecated and doesn’t do anything in config (reader, writer, monitoring.push).
v1.7.2 #
Released: 2023-12-21
- FIX: fit/infer calls are now skipped if we have insufficient valid data to run on.
- FIX: proper handling of
inf
andNaN
in fit/infer calls. - FEATURE: add counter of skipped model runs
vmanomaly_model_runs_skipped
to healthcheck metrics. - FEATURE: add exponential retries wrapper to VmReader’s
read_metrics()
. - FEATURE: add
BacktestingScheduler
for consecutive retrospective fit/infer calls. - FEATURE: add improved & numerically stable anomaly scores.
- IMPROVEMENT: add full config validation. The probability of getting errors in later stages (say, model fit) is greatly reduced now. All the config validation errors that needs to be fixed are now a part of logging.
note: this is an backward-incompatible change, as
model
config section now expects key-value args for internal model defined in nestedargs
. - IMPROVEMENT: add explicit support of
gzip
-ed responses from vmselect in VmReader.
v1.6.0 #
Released: 2023-10-30
- IMPROVEMENT:
- now all the produced healthcheck metrics have
vmanomaly_
prefix for easier accessing. - updated docs for monitoring.
note: this is an backward-incompatible change, as metric names will be changed, resulting in new metrics creation, i.e.
model_datapoints_produced
will becomevmanomaly_model_datapoints_produced
- now all the produced healthcheck metrics have
- IMPROVEMENT: Set default value for
--log_level
fromDEBUG
toINFO
to reduce logs verbosity. - IMPROVEMENT: Add alias
--log-level
to--log_level
. - FEATURE: Added
extra_filters
parameter to reader. It allows to apply global filters to all queries. - FEATURE: Added
verify_tls
parameter to reader and writer. It allows to disable TLS verification for remote endpoint. - FEATURE: Added
bearer_token
parameter to reader and writer. It allows to pass bearer token for remote endpoint for authentication. - BUGFIX: Fixed passing
workers
parameter for reader. Previously it would throw a runtime error ifworkers
was specified.
v1.5.1 #
Released: 2023-09-18
- IMPROVEMENT: Infer from the latest seen datapoint for each query. Handles the case datapoints arrive late.
v1.5.0 #
Released: 2023-08-11
- FEATURE: add
--license
and--license-file
command-line flags for license code verification. - IMPROVEMENT: Updated Python to 3.11.4 and updated dependencies.
- IMPROVEMENT: Guide documentation for Custom Model usage.
v1.4.2 #
Released: 2023-06-09
- FIX: Fix case with received metric labels overriding generated.
v1.4.1 #
Released: 2023-06-09
- IMPROVEMENT: Update dependencies.
v1.4.0 #
Released: 2023-05-06
- FEATURE: Reworked self-monitoring grafana dashboard for vmanomaly.
- IMPROVEMENT: Update python version and dependencies.
v1.3.0 #
Released: 2023-03-21
- FEATURE: Parallelized queries. See
reader.workers
param to control parallelism. By default it’s value is equal to number of queries (sends all the queries at once). - IMPROVEMENT: Updated self-monitoring dashboard.
- IMPROVEMENT: Reverted back default bind address for /metrics server to 0.0.0.0, as vmanomaly is distributed in Docker images.
- IMPROVEMENT: Silenced Prophet INFO logs about yearly seasonality.
v1.2.2 #
Released: 2023-03-19
- FIX: Fix
for
metric label to pass QUERY_KEY. - FEATURE: Added
timeout
config param to reader, writer, monitoring.push. - FIX: Don’t hang if scheduler-model thread exits.
- FEATURE: Now reader, writer and monitoring.push will not halt the process if endpoint is inaccessible or times out, instead they will increment metrics
*_response_count{code=~"timeout|connection_error"}
.
v1.2.1 #
Released: 2023-02-18
- FIX: Fixed scheduler thread starting.
- FIX: Fix rolling model fit+infer.
- BREAKING CHANGE: monitoring.pull server now binds by default on 127.0.0.1 instead of 0.0.0.0. Please specify explicitly in monitoring.pull.addr what IP address it should bind to for serving /metrics.
v1.2.0 #
Released: 2023-02-04
- FEATURE: With arg
--watch
watches for config(s) changes and reloads the service automatically. - IMPROVEMENT: Remove “provide_series” from HoltWinters model. Only Prophet model now has it, because it may produce a lot of series if “holidays” is on.
- IMPROVEMENT: if Prophet’s “provide_series” is omitted, then all series are returned.
- DEPRECATION: Config monitoring.endpoint_url is deprecated in favor of monitoring.url.
- DEPRECATION: Remove ’enable’ param from config monitoring.pull. Now /metrics server is started whenever monitoring.pull is present.
- IMPROVEMENT: include example configs into the docker image at /vmanomaly/config/*
- IMPROVEMENT: include self-monitoring grafana dashboard into the docker image under /vmanomaly/dashboard/vmanomaly_grafana_dashboard.json
v1.1.0 #
Released: 2023-01-23
- IMPROVEMENT: update Python dependencies
- FEATURE: Add multivariate IsolationForest model.
v1.0.1 #
Released: 2023-01-06
- FIX: prophet model incorrectly predicted two points in case of only one
v1.0.0-beta #
Released: 2022-12-08
- First public release is available