lib/handhashke: narrow down condition for legacy connection fallback

vmstorage closes connection if it receives unexpected hello message.
So it makes sense only to perform fall-back into the legacy protocol in
this case.

Related to https://github.com/VictoriaMetrics/VictoriaMetrics/issues/11146

deployment/docker/vmanomaly/vmanomaly-integration/compose.yml

@@ -59,7 +59,7 @@ services:
       - '--external.alert.source=explore?orgId=1&left=["now-1h","now","VictoriaMetrics",{"expr": },{"mode":"Metrics"},{"ui":[true,true,true,"none"]}]'
     restart: always
   vmanomaly:
-    image: victoriametrics/vmanomaly:v1.29.7
+    image: victoriametrics/vmanomaly:v1.29.6
     depends_on:
       - "victoriametrics"
     ports:

docs/anomaly-detection/CHANGELOG.md

@@ -14,17 +14,6 @@ aliases:
 ---
 Please find the changelog for VictoriaMetrics Anomaly Detection below.
 
-## v1.29.7
-Released: 2026-06-25
-
-- UI: updated [vmanomaly UI](https://docs.victoriametrics.com/anomaly-detection/ui/) from [v1.7.1](https://docs.victoriametrics.com/anomaly-detection/ui/#v171) to [v1.7.2](https://docs.victoriametrics.com/anomaly-detection/ui/#v172), see respective [release notes](https://docs.victoriametrics.com/anomaly-detection/ui/#v172) for details. Notable mentions include `api/v1/server/model` endpoint for accessing production models config and queries from UI, manually or through [AI assistant](https://docs.victoriametrics.com/anomaly-detection/ui/#ai-assistance).
-
-- IMPROVEMENT: Increased high-cardinality inference scaling by optionally scattering periodic infer jobs to reduce contention on shared resources (e.g. datasource, CPU, RAM) when `settings.n_workers > 1` and `scheduler.infer_every` is smaller than the total time to fetch and process all queries. This is controlled by new `scatter_infer_jobs` boolean argument of [Periodic Scheduler](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/#parameters-1) (default: `false`).
-
-- IMPROVEMENT: Optimized internal batching for reader post-fetch series processing, exposing reader processing queue depth (`vmanomaly_reader_processing_tasks_queued` [metric](https://docs.victoriametrics.com/anomaly-detection/components/monitoring/#reader-behaviour-metrics)), and clarifying inference skip logs after data fetch timeouts. See `series_processing_batch_size` argument of [VmReader](https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader) and [VLogsReader](https://docs.victoriametrics.com/anomaly-detection/components/reader/#victorialogs-reader) for details.
-
-- IMPROVEMENT: Refined `VmReader` and `VLogsReader` logging after datasource request failures by suppressing the follow-up generic "No data" or "No unseen data" warning for failed fetches. Failed requests now keep the original datasource error while empty successful responses still emit the no-data warning.
-
 ## v1.29.6
 Released: 2026-06-17
 

docs/anomaly-detection/FAQ.md

@@ -423,7 +423,7 @@ services:
   # ...
   vmanomaly:
     container_name: vmanomaly
-    image: victoriametrics/vmanomaly:v1.29.7
+    image: victoriametrics/vmanomaly:v1.29.6
     # ...
     restart: always
     volumes:
@@ -641,7 +641,7 @@ options:
 Here’s an example of using the config splitter to divide configurations based on the `extra_filters` argument from the reader section:
 
 ```sh
-docker pull victoriametrics/vmanomaly:v1.29.7 && docker image tag victoriametrics/vmanomaly:v1.29.7 vmanomaly
+docker pull victoriametrics/vmanomaly:v1.29.6 && docker image tag victoriametrics/vmanomaly:v1.29.6 vmanomaly
 ```
 
 ```sh

docs/anomaly-detection/Migration.md

@@ -45,7 +45,7 @@ There are 2 types of compatibility to consider when migrating in stateful mode:
 
 | Group start | Group end | Compatibility | Notes |
 |---------|--------- |------------|-------|
-| [v1.29.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1291) | [v1.29.7](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1297) | Fully Compatible | - |
+| [v1.29.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1291) | [v1.29.6](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1296) | Fully Compatible | - |
 | [v1.28.7](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1287) | [v1.29.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1290) | Partially compatible* | Dumped models of class [prophet](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet) and [seasonal quantile](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-seasonal-quantile) have problems with loading to [v1.29.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1290) due to dropped `pytz` library. **Upgrading directly from v1.28.7 to [v1.29.1](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1291) with a fix is suggested** |
 | [v1.26.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1262) | [v1.28.7](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1287) | Fully Compatible | [v1.28.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1280) introduced [rolling](https://docs.victoriametrics.com/anomaly-detection/components/models/#rolling-models) model class drop in favor of [online](https://docs.victoriametrics.com/anomaly-detection/components/models/#online-models) models (`rolling_quantile` and `std` models), however, it does not impact compatibility, as artifacts were not produced by default for rolling models. Also, offline `mad` and `zscore` models are redirecting to their respective online counterparts since [v1.28.4](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1284). |
 | [v1.25.3](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1253) | [v1.26.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1270) | Partially Compatible* | [v1.25.3](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1253) introduced `forecast_at` argument for base [univariate](https://docs.victoriametrics.com/anomaly-detection/components/models/#univariate-models) and `Prophet` [models](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet), however, itself remains backward-reversible from newer states like [v1.26.2](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1262), [v1.27.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1270). (All models except `isolation_forest_multivariate` class will be dropped) |

docs/anomaly-detection/QuickStart.md

@@ -132,7 +132,7 @@ Below are the steps to get `vmanomaly` up and running inside a Docker container:
 1. Pull Docker image:
 
 ```sh
-docker pull victoriametrics/vmanomaly:v1.29.7
+docker pull victoriametrics/vmanomaly:v1.29.6
 ```
 
 2. Create the license file with your license key.
@@ -152,7 +152,7 @@ docker run -it \
     -v ./license:/license \
     -v ./config.yaml:/config.yaml \
     -p 8490:8490 \
-    victoriametrics/vmanomaly:v1.29.7 \
+    victoriametrics/vmanomaly:v1.29.6 \
     /config.yaml \
     --licenseFile=/license \
     --loggerLevel=INFO \
@@ -169,7 +169,7 @@ docker run -it \
     -e VMANOMALY_DATA_DUMPS_DIR=/tmp/vmanomaly/data \
     -e VMANOMALY_MODEL_DUMPS_DIR=/tmp/vmanomaly/models \
     -p 8490:8490 \
-    victoriametrics/vmanomaly:v1.29.7 \
+    victoriametrics/vmanomaly:v1.29.6 \
     /config.yaml \
     --licenseFile=/license \
     --loggerLevel=INFO \
@@ -182,7 +182,7 @@ services:
   # ...
   vmanomaly:
     container_name: vmanomaly
-    image: victoriametrics/vmanomaly:v1.29.7
+    image: victoriametrics/vmanomaly:v1.29.6
     # ...
     restart: always
     volumes:
@@ -267,7 +267,6 @@ schedulers:
     # https://docs.victoriametrics.com/anomaly-detection/components/scheduler/#periodic-scheduler
     class: 'periodic'
     infer_every: '5m'
-    scatter_infer_jobs: true
     fit_every: '1d'
     fit_window: '4w'
 
@@ -299,7 +298,6 @@ reader:
   datasource_url: "https://play.victoriametrics.com/" # [YOUR_DATASOURCE_URL]
   tenant_id: '0:0'
   sampling_period: "5m"
-  series_processing_batch_size: 8  # number of time series to process together while preparing data for fit or infer stages
   queries:
     # define your queries with MetricsQL - https://docs.victoriametrics.com/victoriametrics/metricsql/
     cpu_user:
@@ -415,13 +413,11 @@ For optimal service behavior, consider the following tweaks when configuring `vm
 - Configure the **inference frequency** in the [scheduler](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/) section of the configuration file.
 - Ensure that `infer_every` aligns with your **minimum required alerting frequency**.
   - For example, if receiving **alerts every 15 minutes** is sufficient (when `anomaly_score > 1`), set `infer_every` to match `reader.sampling_period` or override it per query via `reader.queries.query_xxx.step` for an optimal setup.
-- Set `scheduler.scatter_infer_jobs` {{% available_from "v1.29.7" anomaly %}} [arg](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/#parameters-1) to `true` to allow for equal distribution of inference jobs across `infer_every` intervals, which can further enhance parallel processing efficiency and reduce resource contention when `reader.queries` contains a large number of queries.
 
 **Reader**:
 - Setup the datasource to read data from in the [reader](https://docs.victoriametrics.com/anomaly-detection/components/reader/) section. Include tenant ID if using a [cluster version of VictoriaMetrics](https://docs.victoriametrics.com/victoriametrics/cluster-victoriametrics/) (`multitenant` value {{% available_from "v1.16.2" anomaly %}} can be also used here).
 - Define queries for input data using [MetricsQL](https://docs.victoriametrics.com/victoriametrics/metricsql/) under `reader.queries` section. Note, it's possible to override reader-level arguments at query level for increased flexibility, e.g. specifying per-query [timezone](https://docs.victoriametrics.com/anomaly-detection/faq/#handling-timezones) or [sampling period](https://docs.victoriametrics.com/anomaly-detection/components/reader/#config-parameters).
 - For longer `fit_window` intervals in scheduler, consider splitting queries into smaller time ranges to avoid excessive memory usage, timeouts and hitting server-side constraints, so they can be queried separately and reconstructed on `vmanomaly` side. Please refer to this [example](https://docs.victoriametrics.com/anomaly-detection/faq/#handling-large-queries-in-vmanomaly) for more details.
-- Set `reader.series_processing_batch_size` {{% available_from "v1.29.7" anomaly %}} [arg](https://docs.victoriametrics.com/anomaly-detection/components/reader/#config-parameters) to a reasonable value (4-16, default is 8) to balance between memory usage and processing speed when preparing data for fit or infer stages.
 
 > If applicable - consider [`VLogsReader`](https://docs.victoriametrics.com/anomaly-detection/components/reader/#victorialogs-reader) {{% available_from "v1.26.0" anomaly %}} to perform anomaly detection on **log-derived metrics**. This is particularly useful for scenarios where log data needs to be analyzed for unusual patterns or behaviors, such as error rates or request latencies.
 

docs/anomaly-detection/UI.md

@@ -315,7 +315,7 @@ docker run -it --rm \
   -e VMANOMALY_MCP_SERVER_URL=http://mcp-vmanomaly:8081/mcp \
   -p 8080:8080 \
   -p 8490:8490 \
-  victoriametrics/vmanomaly:v1.29.7 \
+  victoriametrics/vmanomaly:v1.29.6 \
   vmanomaly_config.yaml
 ```
 
@@ -640,21 +640,6 @@ If the **results** look good and the **model configuration should be deployed in
 
 ## Changelog
 
-### v1.7.2
-Released: 2026-06-25
-
-vmanomaly version: [v1.29.7](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1297)
-
-- FEATURE: Added controls for selecting server-configured scheduled models (drop-down inside [model wizard](#model-panel)) and browsing scheduled queries from the running vmanomaly instance ("Queries" button, "scheduled queries" tab).
-
-- IMPROVEMENT: Surfaced datasource fetch failures from ad-hoc VMUI raw queries as query-level errors instead of returning a successful empty result that triggers a generic "No match" warning. Now the user can see the actual error message from the datasource (e.g. "unauthorized", "not found", etc.) and take appropriate action.
-
-- BUGFIX: Fixed [UI/query-server](#settings-panel) handling of VictoriaMetrics datasource URLs that already include `/select/multitenant/prometheus`. Such URLs are now recognized as cluster datasource URLs, preserving the multitenant path when proxying VMUI requests and allowing `server.use_reader_connection_settings` to reuse [configured reader credentials for authenticated datasources](#authentication).
-
-- BUGFIX: Fixed [settings](#settings-panel) inputs for server and datasource URLs so editing, deleting, or pasting text is no longer immediately reverted to the previous value before applying changes.
-
-- BUGFIX: Fixed [model wizard](#model-panel) settings for [`IsolationForestModel`](https://docs.victoriametrics.com/anomaly-detection/components/models/#isolation-forest-multivariate) `contamination`, allowing decimal float values such as `0.1` or `0,1` to be typed or pasted without being collapsed to `0`, while preserving the `"auto"` value.
-
 ### v1.7.1
 Released: 2026-06-11
 

docs/anomaly-detection/components/README.md

@@ -49,7 +49,6 @@ schedulers:
   periodic_online:  # alias
     class: 'periodic' # scheduler class
     infer_every: "30s"  # how often to produce anomaly scores for new data
-    scatter_infer_jobs: true  # distribute infer jobs evenly across the infer interval to reduce synchronized bursts
     fit_every: "365d"  # how often to re-fit the models, for online models used effectively once, then they are updated with new data and won't require re-fit
     fit_window: "3d"  # how much historical data to use for fit stage
     start_from: "00:00"  # start from specified time, i.e. 00:00 given timezone and do daily fits as `fit_every` is 1 day
@@ -57,7 +56,6 @@ schedulers:
   periodic_offline_1w:
     class: 'periodic'
     infer_every: "15m"
-    scatter_infer_jobs: true
     fit_every: "24h"
     fit_window: "14d"
     # if no start_from is specified, jobs will start immediately after service starts
@@ -137,7 +135,6 @@ server:
   port: 8490
   path_prefix: '/vmanomaly'  # optional path prefix for all HTTP routes
   max_concurrent_tasks: 4  # maximum number of concurrent anomaly detection tasks processed by backend
-  use_reader_connection_settings: True  # if True, use reader's datasource_url and credentials for UI requests to datasource
   uvicorn_config:  # optional Uvicorn server configuration
     log_level: 'warning'
 ```

docs/anomaly-detection/components/models.md

@@ -1265,7 +1265,7 @@ monitoring:
 Let's pull the docker image for `vmanomaly`:
 
 ```sh
-docker pull victoriametrics/vmanomaly:v1.29.7
+docker pull victoriametrics/vmanomaly:v1.29.6
 ```
 
 Now we can run the docker container putting as volumes both config and model file:
@@ -1279,7 +1279,7 @@ docker run -it \
 -v $(PWD)/license:/license \
 -v $(PWD)/custom_model.py:/vmanomaly/model/custom.py \
 -v $(PWD)/custom.yaml:/config.yaml \
-victoriametrics/vmanomaly:v1.29.7 /config.yaml \
+victoriametrics/vmanomaly:v1.29.6 /config.yaml \
 --licenseFile=/license
 --watch
 ```

docs/anomaly-detection/components/monitoring.md

@@ -458,21 +458,6 @@ Label names [description](#labelnames)
             <td>The total number of datapoints received from VictoriaMetrics for the `query_key` query within the specified scheduler `scheduler_alias`, in the `vmanomaly` service running in `preset` mode.</td>
             <td>
 
-`url`, `query_key`, `scheduler_alias`, `preset`
-            </td>
-        </tr>
-        <tr>
-            <td>
-
-<span style="white-space: nowrap;">`vmanomaly_reader_processing_tasks_queued`</span>
-            </td>
-            <td>
-
-`Gauge`
-            </td>
-            <td>The total number of queued processing tasks {{% available_from "v1.29.7" anomaly %}} (timeseries batches of size `series_processing_batch_size`) for the `query_key` query within the specified scheduler `scheduler_alias`, in the `vmanomaly` service running in `preset` mode. If continuously >0, it may lead to skipped infer runs due to resource contention and timeouts.</td>
-            <td>
-
 `url`, `query_key`, `scheduler_alias`, `preset`
             </td>
         </tr>

docs/anomaly-detection/components/reader.md

@@ -421,20 +421,7 @@ Optional argument{{% available_from "v1.18.1" anomaly %}} allows defining **vali
 `60s`
             </td>
             <td>
-Optional argument {{% available_from "v1.25.3" anomaly %}}, allows specifying a time offset for all queries in `queries`. Defaults to `0s` (0) if not set and can be overridden on a [per-query basis](#per-query-parameters).
-            </td>
-        </tr>
-        <tr>
-            <td>
-
-<span style="white-space: nowrap;">`series_processing_batch_size`</span>
-            </td>
-            <td>
-
-`8`
-            </td>
-            <td>
-Optional argument {{% available_from "v1.29.7" anomaly %}}, allows specifying the number of time series to process together while preparing data for fit or infer stages. Defaults to `8`. Suggested values are 4-16 for high-cardinality queries.
+Optional argument{{% available_from "v1.25.3" anomaly %}} allows specifying a time offset for all queries in `queries`. Defaults to `0s` (0) if not set and can be overridden on a [per-query basis](#per-query-parameters).
             </td>
         </tr>
     </tbody>
@@ -463,7 +450,6 @@ reader:
   sampling_period: '1m'
   query_from_last_seen_timestamp: True  # false by default
   latency_offset: '1ms'
-  series_processing_batch_size: 8
 ```
 
 ### MetricsQL Playground
@@ -893,19 +879,6 @@ If a path to a CA bundle file (like `ca.crt`), it will verify the certificate us
 (Optional) Password for authentication. If set, it will be used to authenticate the request.
             </td>
         </tr>
-        <tr>
-            <td>
-
-<span style="white-space: nowrap;">`series_processing_batch_size`</span>
-            </td>
-            <td>
-
-`8`
-            </td>
-            <td>
-Optional argument {{% available_from "v1.29.7" anomaly %}}, allows specifying the number of time series to process together while preparing data for fit or infer stages. Defaults to `8`. Suggested values are 4-16 for high-cardinality queries.
-            </td>
-        </tr>
     </tbody>
 </table>
 
@@ -924,7 +897,6 @@ reader:
   # tenant_id: '0:0'  # for cluster version only
   sampling_period: '1m'
   max_points_per_query: 10000
-  series_processing_batch_size: 8
   data_range: [0, 'inf']  # reader-level
   offset: '0s'  # reader-level
   timeout: '30s'

docs/anomaly-detection/components/scheduler.md

@@ -74,7 +74,40 @@ options={`"scheduler.periodic.PeriodicScheduler"`, `"scheduler.oneoff.OneoffSche
 
 ### Parameters
 
-For periodic scheduler parameters are defined as differences in times, expressed in difference units, e.g. days, hours, minutes, seconds. Time granularity is defined by the last characters of a string. Examples: `"50s"` (seconds), `"4m"` (minutes), `"3h"` (hours), `"2d"` (days), `"1w"` (weeks).
+For periodic scheduler parameters are defined as differences in times, expressed in difference units, e.g. days, hours, minutes, seconds.
+
+Examples: `"50s"`, `"4m"`, `"3h"`, `"2d"`, `"1w"`. 
+
+<table class="params">
+    <thead>
+        <tr>
+            <th></th>
+            <th>Time granularity</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>s</td>
+            <td>seconds</td>
+        </tr>
+        <tr>
+            <td>m</td>
+            <td>minutes</td>
+        </tr>
+        <tr>
+            <td>h</td>
+            <td>hours</td>
+        </tr>
+        <tr>
+            <td>d</td>
+            <td>days</td>
+        </tr>
+        <tr>
+            <td>w</td>
+            <td>weeks</td>
+        </tr>
+    </tbody>
+</table>
 
 <table class="params">
     <thead>
@@ -155,21 +188,6 @@ Specifies when to initiate the first `fit_every` call. Accepts either an ISO 860
 Defines the local timezone for the `start_from` parameter, if specified. Defaults to `UTC` if no timezone is provided.
             </td>
         </tr>
-        <tr>
-            <td>
-
-<span style="white-space: nowrap;">`scatter_infer_jobs`{{% available_from "v1.29.7" anomaly %}}</span>
-            </td>
-            <td>bool, <span style="white-space: nowrap;">Optional</span></td>
-            <td>
-
-`true` or `false`
-            </td>
-            <td>
-
-If `true`, distribute infer jobs and their dependent data-fetch jobs evenly across the infer interval. This reduces synchronized read and inference bursts for high-scale configurations. Defaults to `false`. Useful when `settings.n_workers > 1`, `reader.queries` cardinality is high, and `scheduler.infer_every` is small.
-            </td>
-        </tr>
     </tbody>
 </table>
 
@@ -182,7 +200,6 @@ schedulers:
     # (or class: "scheduler.periodic.PeriodicScheduler" for versions before v1.13.0, without class alias support)
     fit_window: "14d" 
     infer_every: "1m" 
-    scatter_infer_jobs: true  # Distribute infer jobs evenly across the infer interval to reduce synchronized bursts.
     fit_every: "1h"
     start_from: "20:00"  # If launched before 20:00 (local Kyiv time), the first run starts today at 20:00. Otherwise, it starts tomorrow at 20:00.
     tz: "Europe/Kyiv"  # Defaults to 'UTC' if not specified.

docs/anomaly-detection/guides/guide-vmanomaly-vmalert/README.md

@@ -395,7 +395,7 @@ services:
     restart: always
   vmanomaly:
     container_name: vmanomaly
-    image: victoriametrics/vmanomaly:v1.29.7
+    image: victoriametrics/vmanomaly:v1.29.6
     depends_on:
       - "victoriametrics"
     ports:

lib/handshake/handshake.go

@@ -43,7 +43,8 @@ func VMInsertClientWithDialer(dial func() (net.Conn, error), compressionLevel in
 		return bc, nil
 	}
 	_ = c.Close()
-	if !strings.Contains(err.Error(), "cannot read success response after sending hello") {
+	// fallback only if vmstorage closed connection at read success response
+	if !errors.Is(err, io.EOF) && !strings.Contains(err.Error(), "cannot read success response after sending hello") {
 		return nil, err
 	}
 	// try to fallback to the prev non-RPC API version