successfully configured data-bricks cluster with Azure log analytics and i could see the following tables and respective log messages in log analytics but i'm not sure how to create a reports of out as i know KQL.
Can you please help to write sample query to get the following metrics by using the tables.
SparkListenerEvent_CL
SparkLoggingEvent_CL
SparkMetrics_CL
how many messages processed per sec?
no of inputs message and output message for sec?
Messages Latency ?
input vs out put message rate
no. of message processing rate per sec
++++++++++++++++++++++++
Azure Databricks is a fast, powerful Apache Spark based analytics service that makes it easy to rapidly develop and deploy big data analytics and artificial intelligence solutions.
Connecting Azure Databricks with Log Analytics allows monitoring and tracing each layer within Spark workloads, including the performance and resource usage on the host as well as Spark metrices.
When you enable diagnostic setting for databricks, it will create 3 tables SparkMetrics, SparkLoggingEvent and SparkListenerEvent for logging spark related events.
Spark metrics
Spark metrics are automatically collected into the SparkMetric_CL Log Analytics custom log. We can use simple KQL query to work on them as shown below.
SparkMetric_CL
| where name_s contains "executor.cpuTime"
| extend sname = split(name_s, ".")
| extend executor=strcat(sname[0], ".", sname[1])
| project TimeGenerated, cpuTime=count_d / 100000
Structured Streaming metrics
Streaming job metrics are automatically collected into the SparkListenerEvent_CL Log Analytics custom log. Similar to the one mentioned above we can use simple KQL query to work on them as shown in the example below.
SparkListenerEvent_CL
| where Event_s contains "queryprogressevent"
| extend sname=strcat(progress_name_s,"-","triggerexecution")
| summarize percentile(progress_duration_Ms_triggerExecution_d, 90) by bin (TimeGenerated, 1m), sname
| order by TimeGenerated asc null last
Spark logs
Spark logs are available in the Databricks UI and can be delivered to a storage account. However, Log Analytics is a much more convenient log store since it indexes the logs at high scale and supports a powerful query language. Spark logs are automatically collected into the SparkLoggingEvent_CL Log Analytics custom log. Following are the example of simple KQL query.
SparkLoggingEvent_CL
| project Message
| limit 50
There are also some prebuilt query names for retrieving Spark metrics in this Access prebuilt queries section of the Microsoft document it will help you solve the remaining problems. When you open log analytics workspace and go to logs, it will open a query dialog. There you can search this prebuild queries.
I would suggest to read these Stream processing with Azure Databricks
and Use Kusto queries documents for more information.
Related
I'm using azure functions which is integrated with application insights. While exporting the data from application insights , I'm able to see only 1898 records. How do I get complete logging information data exported to excel without creating azure data explorer cluster (I don't have permissions to create it)
You can achieve this by running a kql query in the logs of Application Insights from Function App
From the Azure Portal,
Go to Function App -> ApplicationInsights -> Logs and
Execute the below Kql query:
union
availabilityResults,
requests,
exceptions,
pageViews,
traces,
customEvents,
dependencies
| where timestamp > datetime("2022-10-14T12:02:57.123Z") and timestamp < datetime("2022-10-15T12:02:57.123Z")
| order by timestamp desc
Log information from Application Insights:
Refer: How to use Union Operator
If Jmeter already connect to Azure(like JMeter logs sent to a platform
like Log Analytics workspace) , you get all Jmeter data you want. You can easily use KQL to query the Jmeter data.
But you just don't know how to query Jmeter Graph - Active Threads Over times
Is there any Query code for it? Thanks
Hi I'm Charlie from the Microsoft for Founders Hub team. I'm not usually here, so may not see a follow up question, but do want help.
KQL is used to query telemetry and logs from technologies based on Azure Data Explorer (i.e,Application Insights, Logs Analytics workspace, Search in Sharepoint).
This said, you must have your JMeter logs sent to a platform like Log Analytics workspace before you can continue to query them. If you have, please follow these link to learn how to interact with that workspace in Azure:
https://learn.microsoft.com/en-us/azure/azure-monitor/logs/log-query-overview
https://learn.microsoft.com/en-us/azure/azure-monitor/logs/log-analytics-tutorial
To help you further, please include how you connected JMeter Logs to Azure.
I am working on Azure Monitor Dashboards.
I need to check the Health Status of my App Service.
If i run use option Metrics (2nd Image) add Health Status metrics and create chart
vs
If i run query on AzureMetrics Table will both return same result? I mean HOW both options are different from each other?
Both use the same source. The difference is that using the "Metrics" blade you can create charts withouth having to write queries using Kusto and anyone with basic knowledge can quickly create charts.
When using the "Logs" blade you have to write a query using Kusto to get the desired results and format the chart manually but you have more control in what and how data is displayed.
If i run query on AzureMetrics Table will both return same result? I
mean HOW both options are different from each other?
The difference between logs and metrics is,
Metrics reveal a service or application's tendencies and proclivities,
while logs focus on specific events. The goal of logs is to save as
much information—mostly technical informations—as possible about a
single event. Log data can be used to investigate occurrences and
assist with root-cause analysis of problems or defects, as well as an
increasing number of other applications.
For more information please refer the below links:-
MSFT TECHCOMMUNITY|Difference between Log Analytics and Monitor
Blogs|Azure Monitor and Azure Log Analytics & Logs or Metrics.
This answer summarizes that App Insights (AI) and Log Analytics (LA) are being merged into one service. It also provides a suggestion that new resources in AI can point at LA, so that all your code is in one place.
My question is how can I query across LA and AI resources, given that both exist, and you don't have the time or permissions to change the AI to point at LA.
Using Azure Workbooks I realised I can query from multiple resources inside LA or AI, but I don't seem to be able to query across LA and AI in one cell (nor save results between cells.)
At present the only ways I can think to solve this are to query through the API or joining in a PBI report, but both of these are massive overhead to complete exploratory querying. Is there an easier way, ideally whilst staying inside Kusto queries?
Azure Monitor is your one-stop shop for querying across cross-resources.
Previously with Azure Monitor, you could only analyze data from within
the current workspace, and it limited your ability to query across
multiple workspaces defined in your subscription. Additionally, you
could only search telemetry items collected from your web-based
application with Application Insights directly in Application Insights
or from Visual Studio. This also made it a challenge to natively
analyze operational and application data together.
Now you can query not only across multiple Log Analytics workspaces,
but also data from a specific Application Insights app in the same
resource group, another resource group, or another subscription. This
provides you with a system-wide view of your data. You can only
perform these types of queries in Log Analytics.
To reference another workspace in your query, use the workspace identifier, and for an app from Application Insights, use the app identifier.
For example, you can query multiple resources from any of your resource instances, these can be workspaces and apps combined like below.
// crossResource function that scopes my Application Insights resources
union withsource= SourceApp
app('Contoso-app1').requests,
app('Contoso-app2').requests,
app('Contoso-app3').requests,
app('Contoso-app4').requests,
app('Contoso-app5').requests
Or like,
union Update, workspace("contosoretail-it").Update, workspace("b459b4u5-912x-46d5-9cb1-p43069212nb4").Update
| where TimeGenerated >= ago(1h)
| where UpdateState == "Needed"
| summarize dcount(Computer) by Classification
Or like,
applicationsScoping
| where timestamp > ago(12h)
| where success == 'False'
| parse SourceApp with * '(' applicationName ')' *
| summarize count() by applicationName, bin(timestamp, 1h)
| render timechart
For details, refer this.
I have Event logs of Application Insights where events are logged and stored as json in text files stored in a blob storage. I need to find those jsons where a customProperty meets a criteria. The number of hit jsons are very less (around 10 or 20), however the data logged is very large. Any suggestions how this can be accomplished efficiently?
I have read in Microsoft documentation that HDInsights understand blob storage and is efficient. Is this relevant in my scenario? If so, could someone provide some starting points.
HDInsight, being a Hadoop-compliant implementation, is a good technology for logs analysis. It is being stated on the AppInsighs page about telemetry as well.
"On larger scales, consider HDInsight - Hadoop clusters in the cloud. HDInsight provides a variety of technologies for managing and analyzing big data."
On the same page, you may find the information about continuous export of AppInsights telemetry into the Azure Blobs storage.
Next step could be to use HDInsight for the analysis of that, but it will need you to implement some kind of algorithm.
For uploading the data to the HDInsight from Azure Blob you may see that link (and this for querying).
For understanding on the logs processing pipeline, which is a common task for Hadoop/HDInsight, some walkthroughs and manuals are available, for example this. But you will need to adjust this algorithm to your scenario.
In case of Application Insights there is another option. New analytic tool Application Insights Analytics has been introduced.
https://blogs.msdn.microsoft.com/bharry/2016/03/28/introducing-application-analytics
This tool also alows you to work with all logged data using the specific language:
requests
| where timestamp >= ago(24h)
| summarize count() by client_CountryOrRegion
| order by count_ desc
You can export data you need.