Apache Kafka scaler: move partition limitation feature to the correct…

… version (kedacore#1020)
raz-bn · Dec 20, 2022 · 30cf86f · 30cf86f
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diff --git a/content/docs/2.10/scalers/apache-kafka.md b/content/docs/2.10/scalers/apache-kafka.md
@@ -33,6 +33,7 @@ triggers:
     scaleToZeroOnInvalidOffset: false
     excludePersistentLag: false
     version: 1.0.0
+    partitionLimitation: '1,2,10-20,31'
 ```
 
 **Parameter list:**
@@ -50,6 +51,7 @@ If 'false' (the default), the scaler will keep a single consumer for that partit
 partition will be scaled to zero. See the [discussion](https://github.com/kedacore/keda/issues/2612) about this parameter.
 - `excludePersistentLag` - When set to `true`, the scaler will exclude partition lag for partitions which current offset is the same as the current offset of the previous polling cycle. This parameter is useful to prevent scaling due to partitions which current offset message is unable to be consumed. If `false` (the default), scaler will include all consumer lag in all partitions as per normal. (Default: `false`, Optional)
 - `version` - Version of your Kafka brokers. See [samara](https://github.com/Shopify/sarama) version (Default: `1.0.0`, Optional)
+- `partitionLimitation` - Comma separated list of partition ids to scope the scaling on. Allowed patterns are "x,y" and/or ranges "x-y". If set, the calculation of the lag will only take these ids into account.  (Default: All partitions, Optional)
 
 > **Note:**
 >

diff --git a/content/docs/2.8/scalers/apache-kafka.md b/content/docs/2.8/scalers/apache-kafka.md
@@ -32,7 +32,6 @@ triggers:
     allowIdleConsumers: false
     scaleToZeroOnInvalidOffset: false
     version: 1.0.0
-    partitionLimitation: '1,2,10-20,31'
 ```
 
 **Parameter list:**
@@ -49,7 +48,6 @@ partitions on a topic, allowing for idle consumers. (Default: `false`, Optional)
 If 'false' (the default), the scaler will keep a single consumer for that partition. Otherwise ('true'), the consumers for that
 partition will be scaled to zero. See the [discussion](https://github.com/kedacore/keda/issues/2612) about this parameter.
 - `version` - Version of your Kafka brokers. See [samara](https://github.com/Shopify/sarama) version (Default: `1.0.0`, Optional)
-- `partitionLimitation` - Comma separated list of partition ids to scope the scaling on. Allowed patterns are "x,y" and/or ranges "x-y". If set, the calculation of the lag will only take these ids into account.  (Default: All partitions, Optional) 
 
 > **Note:**
 >

diff --git a/content/docs/2.9/scalers/apache-kafka.md b/content/docs/2.9/scalers/apache-kafka.md
@@ -33,6 +33,7 @@ triggers:
     scaleToZeroOnInvalidOffset: false
     excludePersistentLag: false
     version: 1.0.0
+    partitionLimitation: '1,2,10-20,31'
 ```
 
 **Parameter list:**
@@ -50,6 +51,7 @@ If 'false' (the default), the scaler will keep a single consumer for that partit
 partition will be scaled to zero. See the [discussion](https://github.com/kedacore/keda/issues/2612) about this parameter.
 - `excludePersistentLag` - When set to `true`, the scaler will exclude partition lag for partitions which current offset is the same as the current offset of the previous polling cycle. This parameter is useful to prevent scaling due to partitions which current offset message is unable to be consumed. If `false` (the default), scaler will include all consumer lag in all partitions as per normal. (Default: `false`, Optional)
 - `version` - Version of your Kafka brokers. See [samara](https://github.com/Shopify/sarama) version (Default: `1.0.0`, Optional)
+- `partitionLimitation` - Comma separated list of partition ids to scope the scaling on. Allowed patterns are "x,y" and/or ranges "x-y". If set, the calculation of the lag will only take these ids into account.  (Default: All partitions, Optional)
 
 > **Note:**
 >