I was creating a recommendation engine in IBM Watson studio for that I needed to add spark service but now it is deprecated what I should use now.
You should utilize spark environments for your Watson studio project.
You can define that spark environment using Environments tab in project and then utilize that runtime when you create notebook or change service for existing notebook.
#Veer as #charles said Spark services are now accessible via Environments. When creating a notebook select a Spark compatible environment and import pyspark in your notebook.
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I am currently using spring boot 2.3.0 version to build an apache spark job in java. This job is working fine in my local. I want to deploy this spring boot spark job on Azure databricks(7.2.0). But while deploying spring boot jar on Azure databricks, I am getting following error -
ava.lang.NoSuchMethodError: org.springframework.core.ResolvableType.forInstance(Ljava/lang/Object;)Lorg/springframework/core/ResolvableType;
at org.springframework.context.event.SimpleApplicationEventMulticaster.resolveDefaultEventType(SimpleApplicationEventMulticaster.java:145)
at org.springframework.context.event.SimpleApplicationEventMulticaster.multicastEvent(SimpleApplicationEventMulticaster.java:127)
at org.springframework.boot.context.event.EventPublishingRunListener.starting(EventPublishingRunListener.java:74)
at org.springframework.boot.SpringApplicationRunListeners.starting(SpringApplicationRunListeners.java:47)
at org.springframework.boot.SpringApplication.run(SpringApplication.java:305)
at org.springframework.boot.SpringApplication.run(SpringApplication.java:1237)
I have checked azure databricks documentation, it has spring core 4.1.3 installed by deafult on azure databricks and in my code the spring core version is 5.2.8. So, I want to ask if there is any way I can upgrade spring core version on azure databricks.
To make third-party or locally-built code available to notebooks and jobs running on your clusters, you can install a library. Libraries can be written in Python, Java, Scala, and R. You can upload Java, Scala, and Python libraries and point to external packages in PyPI, Maven, and CRAN repositories
Steps to install Spring version in Azure Databricks:
Step1: Download the spring core library from the Maven repository. Click on jar file to download.
Step2: Choose the cluster in which you want to install the library.
Libraries => Install New => Library Source: "Upload", Library Type: "Jar", Click on drop here and choose the previously downloaded jar file => Click install.
Successfully, installed spring_core_5_2_8 library on the cluster.
For different methods to install packages in Azure Databricks, refer: How to install a library on a databricks cluster using some command in the notebook?
I am trying to run some spark scripts using execute python script in azure ML studio. And getting an error saying
unable to import spark libraries
Basically i am trying to create web services using ML studio for the models that are developed.
Is it possible or feasible to run spark jobs using ML studio?
Can anyone please help me on this.
Thanks in advance.
I am planning to execute spark from KNIME analytics platform. For this I need to install KNIME spark executors in the KNIME analytics platform.
Can any one please let me know how to install KNIME spark executors in the KNIME analytics platform for hadoop distribution CDH 5.10.X.
I am referring the installation guide from the below link:
https://www.knime.org/knime-spark-executor
I could successfully configure/integrate spark in KNIME.
I did it in CDH 5.7.
I followed the following steps:
1.Downloaded knime-full_3.3.2.linux.gtk.x86_64.tar.gz.
2.Exract the above mentioned pacakge and run installation for KNIME.
3.After KNIME is installed goto File ->Install KNIME Extensions -> Install Bigdata extensions(Check all the Spark related extensions and proceed).
Follow this link:
https://tech.knime.org/installation-instructions#download
4.Till now only the Bigdata related extensions have been installed but they need license to be functional.
5.License needs to be purchased.However,free trail for 30 days can be availed after which it needs to be purchased.
Folow this link :
https://www.knime.org/knime-spark-executor
6.After plugins are installed we need to configure Spark-job-server.
For that we need to download the compatible version of spark-job-server for the hadoop version we have.
Folow this link for version of spark-job-server and its compatible version :
https://www.knime.org/knime-spark-executor
I'm pretty sure it's as easy as registering for the free trial (and buying the license for longer than 30 days) and then installing the software from the Help->Install New Software menu.
As of version KNIME 3.6 (latest), it should be possible to connect to Spark via Livy, no specific executor deployment on a KNIME Server. Still in preview, but it should do it.
https://www.knime.com/whats-new-in-knime-36
I want to upgrade my SPark component to 2.1.0 from its default 2.0.x.2.5 in Ambari.
I am using HDP 2.5.0 with Ambari 2.4.2.
Appreciate any idea to achieve this.
HDP 2.5 shipped with a technical preview of Spark 2.0, and also Spark 1.6.x. If you do not want to use either of those versions and you want Ambari to manage the service for you then you will need to write a custom service for the Spark version that you want. If you don't want ambari to manage the Spark instance you can follow similar instructions as provided on the Hortonworks Community Forum to manually install Spark 2.x without management.
Newer versions of Ambari (maybe 3.0) probably will support per-component upgrade/multiple component versions
See https://issues.apache.org/jira/browse/AMBARI-12556 for details.
Currently I can only see the file HANASPARKCTRL00P_5-70001262.RPM in SAP SWDC under HANA Platform Components. Does it mean that Ambari-managed installation is no longer supported, or I simply miss the required authorisation in the Services Portal?
There is no Ambari installation for Spark Controller 1.5. It is only possible as of Spark Controller 1.6. The installation instructions can be found in the HANA Admin Guide (current version is from 5/11/2016) -> chapter "9.2.3.1 Set up Spark Controller using Ambari" as well as in the attachments of SAP Note 2318054 "SAP HANA Spark Controller 1.6.0 SPS 11 and Higher (Compatible with Spark 1.5.2)".