How can I send metrics from my spark streaming job to open tsdb database? I am trying to use open tsdb as data source in Grafana. Can you please help me with some references where I can start.
I do see open tsdb reporter here which does similar job. How can I integrate the metrics from Spark streaming job to use this? Is there any easy options to do it.
One way to send the metrics to opentsdb is to use it's REST API. To use it, simply convert the metrics to JSON strings and then utilize the Apache Http Client library to send the data (it's in java and can therefore be used in scala). Example code can be found on github.
A more elegant solution would be to use the Spark metrics library and add a sink to the database. There has been a discussion on adding an OpenTSDB sink for the Spark metrics library, however, finally it was not added into Spark itself. The code is avaiable on github and should be possible to use. Unfortunalty the code is compatible on Spark 1.4.1, however, in worst case it should still be possible to get some indications of what is necessary to add.
Related
Small question regarding an integration between Splunk and Apache Spark.
Currently, I am doing a search query in Splunk. The result is quite big. And I am exporting this result as a CSV to share with several teams for downstream work.
Each of the downstream work ended up loading the CSV as part of a Apache Spark job, converting it to DataSet, and doing map reduce on it.
The Spark jobs from each and every teams are different. Therefore, simply plugin each and every teams computation in Splunk directly is not quite scalable.
This is leading us to ask a question, instead of each teams having to download a copy of the CSV, may I ask, if there is an API, or a way to connect to Splunk search result from Apache Spark directly?
Thank you
Splunk does not have an API specifically for Spark. There is a REST API, a few SDKs, and (perhaps best for you) support for ODBC. With an ODBC/JDBC driver installed on your Spark server and a few saved searches defined on Splunk, you should be able to export results from Splunk to Spark for analysis. See https://www.cdata.com/kb/tech/splunk-jdbc-apache-spark.rst for more information.
I am new to both spark and talend.
But I read everywhere that both of these are ETL tools. I read another stackoverflow answer here. From the other answer what I understood is talend do use spark for large data processing. But can talend do all the ETL work efficiently that spark is doing without using spark under the hood? Or is it essentially a wrapper over spark where all the data is send to talend is actually put inside the spark inside talend for processing?
I am quite confused with this. Can someone clarify this?
Unlike Informatica BDM which has its own Blaze framework for processing on Hadoop (native), Talend relies on other frameworks such as Map Reduce (Hadoop using possibly tez underneath) or Spark engine. So you could avoid Spark, but there is less point in doing so. The key point is that we could expect I think some productivity using Talend as it is graphical based, which is handy when there are many fields and you do not need possibly the most skilled staff.
For NOSQL, like HBase, they provide specific connectors or could use the Phoenix route. Talend also has connectors for KAFKA.
Spark is just one of the frameworks supported by Talend. When you create a new job, you can pick Spark from the dropdown list. You can get more details in the docs.
Spark Streaming provided a "Streaming" tab within the deployed Web UI (http://localhost:4040 for running applications or http://localhost:18080 for completed applications, both by default) for each application executed, where graphs representative of application performance could be obtained, which is no more available using Spark Structured Streaming. In my case, I am developing a streaming application with Spark Structured Streaming that reads from a Kafka broker and I would like to obtain a graph of records processed per second, such as the one I could obtain when using Spark Streaming instead of Spark Structured Streaming, among other graphical information.
What is the best alternative to achieve this? I am using Spark 3.0.1 (via pyspark library), and deploying my application on a YARN cluster.
I've checked Monitoring Structured Streaming Applications Using Web UI by Jacek Laskowski, but it is still not very clear how to obtain this type of information in a graphic way.
Thank you in advance!
I managed to get what I wanted. For some reason I still don't know, the Spark History Server UI for completed apps (on http://localhost:18080 by default) did not show the new tab ("Structured Streaming" tab) that is available for Spark Structured Streaming applications that are executed on Spark 3.0.1. However, the web UI that I managed to access through the URL http://localhost:4040 does show me the information that I wanted to retrieve. You just need to click on the 'runId' link of the streaming query from which you want to get the statistics.
If you can't see this tab, based on my personal experience, I recommend the following:
Upgrade to Spark latest version (currently 3.0.1)
Consult this information on the UI deployed at port 4040 while the application is running, instead of port 18080 when the application has finished.
I found the Web UI official documentation from latest Apache Spark very useful to achieve this.
Most metrics informations you see in spark UI is exported by spark.
If spark UI don't fit your requirement, you could retrieve theses metrics and process it.
you can use a sink to export the data, for exemple to csv, prometheus, ... or via rest API.
you should take a look at spark monitoring : https://spark.apache.org/docs/latest/monitoring.html
I have to perform the benchmarking of spark streaming processing. My process gets pulls messages from the kafka, process and loads into ElasticSearch. The upstream generates 100k records per second. So I would like to calculate how many messages processed in 1 second and the latency time. Is there any tools available to monitor this or is there any process to calculate this.
Spark UI can help you,providing the necessary details you need.
By default, the spark ui is available on http://:4040 in a web browser(For a single spark Context).
For the help,you can use this link: http://spark.apache.org/docs/latest/monitoring.html
Beyond the Spark UI, which is useful for determining the rate of processing of your data, you can also use third-party tools like spark-perf to perform load testing on your cluster and obtain benchmark data in that way as well.
Maybe someone should try Yahoo's streaming-benchmarks, I found databricks use that tool to do benchmark between spark streaming and flink.
https://github.com/yahoo/streaming-benchmarks
https://databricks.com/blog/2017/10/11/benchmarking-structured-streaming-on-databricks-runtime-against-state-of-the-art-streaming-systems.html
I have been looking for a way to monitor performance in Spark on Bluemix. I know in the Apache Spark project, they provide a metrics service based on the Coda Hale Metrics Library. This allows users to report Spark metrics to a variety of sinks including HTTP, JMX, and CSV files. Details here: http://spark.apache.org/docs/latest/monitoring.html
Does anyone know of any way to do this in the Bluemix Spark service? Ideally, I would like to save the metrics to a csv file in Object Storage.
Appreciate the help.
Thanks
Saul
Currently, I do not see an option for usage of "Coda Hale Metrics Library" and reporting the job history or accessing the information via REST API.
However, on the main page of the Spark history server, you can see the Event log directory. It refers to your following user directory: file:/gpfs/fs01/user/USER_ID/events/
There I saw JSON (like) formatted files.