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docs/user-guide/management/audit-trail.md

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!!! info "Availability"
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This feature is available in Enterprise Edition and Cloud. Not available in Open Source.
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## What is Audit Trail
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## What is audit trail?
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Audit Trail records user actions across all organizations in OpenObserve. It captures non-ingestion API calls and helps you monitor activity and improve security.
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!!! note "Who can access"

docs/user-guide/metrics/downsampling-metrics.md

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Downsampling summarizes historical data into fewer data points. Each summarized data point is calculated using an aggregation method, such as the last recorded value, the average, or the total, applied over a defined time block.
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## Configure Downsampling
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## Configure downsampling
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Downsampling is configured using the following environment variables.:
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> Refer to the [Downsampling Rule](#downsampling-rule) section. <br>
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#### Downsampling Configuration For Helm Chart Users
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#### Downsampling configuration for Helm Chart users
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Add the environment variables under the `enterprise.parameters` section in your `values.yaml` file:
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```
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`O2_METRICS_DOWNSAMPLING_RULES`: "o2_cpu_usage:avg:30d:5m"
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```
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#### Downsampling Configuration For Terraform Users
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#### Downsampling configuration for Terraform users
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Set the same variables in your `terraform.tfvars` file:
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```
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> **Note**: **After setting the environment variables, make sure to redeploy the OpenObserve instance for the changes to apply.**
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### Downsampling Rule
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### Downsampling rule
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User-defined rules determine how downsampling is applied to metrics streams. You can define multiple downsampling rules to target different streams or use different configurations.
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- **offset**: It defines the age of data eligible for downsampling. For example, 15d for applying downsampling on data older than 15 days.
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- **step**: The time block used to group data points. For example, 30m for applying downsampling to retain one value every 30 minutes.
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### Sample Downsampling Rules
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### Sample downsampling rules
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#### Single Rule
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#### Single rule
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```yaml
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O2_METRICS_DOWNSAMPLING_RULES: "o2_cpu_metrics:avg:30d:5m"
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```
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Retains one average value every 5 minutes for `o2_cpu_metrics` data older than 30 days.<br>
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**Multiple Rules**
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**Multiple rules**
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```yaml
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O2_METRICS_DOWNSAMPLING_RULES: "o2_cpu_metrics:avg:30d:5m, o2_app_logs:last:10d:10m"
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```
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```
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Targets all streams starting with `o2_cpu_`, and for each matching stream, retains one hourly sum for data older than 10 days.
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### Downsampling Example
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### Downsampling example
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**Scenario**<br>
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A system is recording CPU usage data every 10 seconds to the stream `o2_cpu_usage`, generating a large volume of high-resolution metrics. Over time, this data becomes too granular and expensive to store or query efficiently for historical analysis.
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`O2_COMPACT_DOWNSAMPLING_INTERVAL` = "180"
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`O2_METRICS_DOWNSAMPLING_RULES` = "o2_cpu_usage:avg:30d:2m"
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**Input Metrics**<br>
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**Input metrics**<br>
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```json
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{ "timestamp": "2024-03-01 00:08:50", "cpu": 20.2 }
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```
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**Downsampling Time Blocks (Step = 2m) and Average CPU Usage**
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**Downsampling time blocks (Step = 2m) and average CPU usage**
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- Time Block 1: From 00:00:00 to 00:01:59, average CPU usage is 20.55
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- Time Block 2: From 00:02:00 to 00:03:59, average CPU usage is 21.75
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- Time Block 3: From 00:04:00 to 00:05:59, average CPU usage is 20.66
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- Time Block 4: From 00:06:00 to 00:07:59, average CPU usage is 21.65
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- Time Block 5: From 00:08:00 to 00:09:59, average CPU usage is 20.88 (not processed yet)
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**Downsampling Job Runs and Outputs**
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**Downsampling job runs and outputs**
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Job 1 runs at 00:03:00 and processes Time Block 1 <br>
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Output:

docs/user-guide/metrics/file-access-time-metric.md

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Analyze file access age in OpenObserve to gauge query performance. Buckets
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track how recently files were accessed, revealing hot vs. cold data trends.
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## What Is File Access Time Metric?
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## What is file access time metric?
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This histogram metric helps analyze the age of files accessed by the querier. This helps in understanding the distribution of file access times across queries and evaluating system performance.
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## How Does It Works?
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## How does it work?
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The metric tracks file age in hourly buckets ranging from 1 hour to 32 hours. Each data point represents how long ago a file was accessed during query execution.
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**The metric is exposed as:**
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Zo_file_access_time_bucket
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```
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## Example Usage
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## Example usage
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To calculate the 95th percentile of file access age for logs over a 5-minute window:
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```

docs/user-guide/streams/.pages

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- Schema Settings: schema-settings.md
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- Extended Retention: extended-retention.md
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- Summary Streams: summary-streams.md
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- Field and Index Types in Streams: fields-and-index-in-streams.md
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- Field and Index Types in Streams: fields-and-index-in-streams.md
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- Query Recommendations Stream: query-recommendations.md
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docs/user-guide/streams/index.md

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- ### [Schema Settings](schema-settings.md)
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- ### [Extended Retention](extended-retention.md)
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- ### [Summary Streams](summary-streams.md)
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- ### [Data Type and Index Types in Streams](data-type-and-index-type-in-streams.md)
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- ### [Data Type and Index Types in Streams](data-type-and-index-type-in-streams.md)
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- ### [Field and Index Types in Streams](fields-and-index-in-streams.md)
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- ### [Query Recommendations Stream](query-recommendations.md)

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