Integrating Kyligence Analytics Platform with Microsoft Azure HDInsight


This is a guest blog from Shaofeng Shi, Senior Architect from Kyligence Inc.

Introducing Kyligence Analytics Platform

Kyligence Analytics Platform (KAP) is an enterprise-ready big data warehouse on Apache Hadoop. Created by the same development team of Apache Kylin, an open-source distributed OLAP engine for big data, KAP inherits all Kylin’s advantages and has more innovations, including but not limiting to:

  • High performance: sub-second query latency over TB to PB data.
  • Ease of Use: drag & drop modeling; JDBC/ODBC drivers for BI integration.
  • Cost Effective: build once to get most queries far faster than before.

KAP reads source data from Apache Hive or Apache Kafka, builds data Cube with Hadoop MapReduce or Apache Spark, and persists the Cube in Apache HBase or KyStorage (an optimized columnar storage engine). The BI tools, dashboards, or web applications send SQL queries to KAP via JDBC/ODBC drivers or RESTful API, and KAP will translate it to Cube visiting and then return results in low latency.

Figure 1 shows the query performance of Apache Kylin (same core as KAP) with the Star Schema Benchmark dataset on 10 million, 20 million, and 40 million rows data. All the queries are finished in less than 1 second, and the performance is stable as the data grows.

Figure 1. SSB Query Performance

With the pre-calculation technology, Apache Kylin is more than 300x faster than Apache Hive in the SSB scenario, as the Figure 2  shows. For more information, you can check https://github.com/Kyligence/ssb-kylin .

Figure 2. Performance of Kylin vs Hive

Azure HDInsight

Backed by a 99.9% SLA, Microsoft Azure HDInsight is the only fully-managed cloud Apache Hadoop offering that gives you optimized open-source analytic clusters for Spark, Hive, MapReduce, HBase, Storm, Kafka, and Microsoft R Server. This architecture separates computing and storage, and since the data is persisted in Azure Blob Storage or Azure Data Lake, customers can easily start/stop and scale/shrink the cluster on demand without worrying about data loss. Azure Blob Storage and Azure Data Lake are highly available, highly scalable, high capable, HDFS compatible, low-cost storage services.

KAP on HDInsight

With Azure HDInsight, you can easily deploy KAP in a few minutes. KAP can run on any node of the cluster, communicating with Hadoop services (Hive/YARN/MapReduce/HBase, etc) via standard protocols. KAP automatically finds the services with the client configurations and then starts working. No additional installation/configuration is needed on Hadoop cluster. If you want to uninstall, a “kylin.sh stop” command shuts down KAP and releases all the resources from cluster.

HDInsight application platform is an easy way to distribute, discover, and install applications that you have built for the Hadoop ecosystem. HDInsight application platform automatically provisions an “Edge Node” for the application, and installs/configures the Hadoop clients on it. Developers only need to provide a shell script to install the application, and other processes are handled by Azure. This is a fancy feature that other Cloud-based Hadoop platform don’t have; This makes deploying KAP on HDInsight becomes very easy and efficient. Figure 3 shows the architecture of KAP with HDInsight.

Figure 3. KAP with Azure HDInsight Architecture

By referring to the samples in Azure Iaas-Applications project, we developed the first version of the Azure Resource Manager (ARM) template for KAP, and successfully deployed KAP on a cluster. After that, we continuously enhance the template and scripts to enable more features. Now, both KAP and KyAnalyzer, our agile BI tool, can be installed as HDInsight applications. Users can quickly get a Hadoop-based Data Warehouse + Business Intelligence tool in a couple of minutes.

Advanced users expect to optimize Hadoop and KAP to have better performance. With Apache Ambari as the centralized configuration management and monitoring platform, users can easily tune the HDInsight parameters and then sync them to all nodes. KAP reads the Hadoop configurations at runtime, so most of the Hadoop changes can take effective immediately without restarting KAP. For KAP/Kylin related configurations, users can login into the edge node with SSH, which is also easy to do.

How to Install KAP

KAP is now available on Microsoft Azure. Our customers have already started to use KAP for their business. We, Kyligence Inc., are also using it in development, testing, training, live demo, prof of concept and other scenarios. To enable it, you just need a couple of steps:

1) You need to prepare a HDInsight cluster whose type is “HBase”. It can be an existing cluster or a new cluster.

Figure 4. Select “HBase” as cluster type

2) If it is a new cluster, you need to switch to the “Custom” wizard, select “Kyligence Analytics Platform” in the third step, as below:

Figure 5. Select KAP to install

If it is an existing HBase cluster, you need to click the “Applications” icon, and then click “Add” button. In the application list, select “Kyligence Analytics Platform” to install. Clicking “Purchase” button means you agree with the terms to install. It won’t cause additional charge for KAP; You only need to pay for the Azure resources that you’re using. The installed KAP has a free trial license. After the license expires, you can contact Kyligence to by a commercial license.

Figure 6. KAP in HDInsight Applications

3) After the cluster is created, you will get the KAP and KyAnalyzer URL from Azure Portal. Clicking them will lead you to the KAP web portal and KyAnalyzer web portal. You can also get the SSH endpoint of the Edge Node that running KAP, so that you can login the Linux command line remotely.

Figure 7. KAP and KyAnalyzer URL

The initial administrator username is “ADMIN” and the password is “KYLIN”. Please remember to reset the passwords on the first login for all the default users (ADMIN, ANALYST and MODELER). You can do this in the “System” -> “User” page.  See the diagram below. You can also add new users and grant roles to them.

Figure 8. Reset initial passwords

KyAnalyzer integrates with KAP for user authentication, so you just need update the user in KAP once, and then use it in KyAnalyzer.

Play with Sample Cube

The installation will automatically create a sample Cube (as well as sample tables in Apache Hive) for you. The sample “kylin_sales” cube will start to build on KAP installed.

Figure 9. Sample Cube

You can monitor the build progress on KAP’s “Monitor” tab; After the build is finished (progress 100%), the Cube state becomes “Ready”, it means you can query the Cube with ANSI-SQL in the “Insight” page, e.g:

select part_dt, sum(price) as total_selled, count(distinct seller_id) as sellers from kylin_sales group by part_dt order by part_dt;

KAP will return results quickly. You can run the same query in to compare the performance. 

Figure 10. Run Query in KAP

Now you know how to use KAP to accelerate your data analysis. But writing SQL is still troublesome for most users. You can use KyAnalyzer to analyze the data by drag-and-drop.

Import Data to KAP

KAP supports Apache Hive and Apache Kafka as the data source. Hive is for batch processing; Kafka is for streaming processing.

To use your batch data in KAP, you need to describe your files as a Hive tables as the first step.  HDInsight supports using Azure Blob Store and Azure Data Lake as the storage for Hadoop, so you can easily manage and process the data on Cloud with high availability, long durability, and low-cost. Here is an example of uploading files to Azure Blob Store with command line:

export AZURE_STORAGE_ACCOUNT=<your storage account>
export AZURE_STORAGE_ACCESS_KEY=<your storage account access key>
# list all files in container
azure storage blob list <container>
# upload a file to container
azure storage blob upload <path of a local file> <container> <name in container>

Azure BLOB Storage uses “/” as the separator in the file name to simulate the folder structures. The following command will upload the local file “airline_2015_01.csv” to container “mycontainer”, and use “airline/2015/airline_2015_01.csv” as the remote path:

azure storage blob upload airline_2015_01.csv mycontainer airline/2015/airline_2015_01.csv

Once the files be uploaded to Azure Blob Store, you can use HiveQL to create the table. You can do this in the “Hive view” of HDInsight Ambari, or use the Hive command line on a HDInsight node. Below is an example of creating a partitioned Hive table with CSV files from Azure Blob Store:

hive> CREATE EXTERNAL TABLE airline_data (
   Year int,  
   Quarter int, 
   Month int,  
   DayofMonth int, 
   DayOfWeek int, 
   FlightDate date, 
   ...
 ) 
 PARTITIONED BY (Part_year STRING)
 ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.OpenCSVSerde'
 WITH SERDEPROPERTIES ("separatorChar" = ",") 
 LOCATION 'wasb://<container>@<storage-account>.blob.core.windows.net/airline' 
 TBLPROPERTIES('serialization.null.format'='','skip.header.line.count'='1');

Then you can add partitions like this:

hive> ALTER TABLE airline_data ADD PARTITION (Part_year = '2015') location 'wasb://<container>@<storage-account>.blob.core.windows.net/airline/2015';

When the table created and has at least 1 partition, you can query it with SQL:

hive> select * from airline_data limit 100;

Now you have successfully created a Hive table with your data files on Azure cloud storage. You can login KAP web GUI, in the “Model” -> “Data Source” page, click the “Load Hive Table” button to import table metadata into KAP:

Figure 11. Import Hive Table

The import only synchronizes the table metadata, like column name. The source data is still in origin place, so this operation will be fast.

To use Apache Kafka as the data source, you need have a running Kafka cluster (v0.10+), which can be connected from the HDInsight cluster that running KAP. That means your Kafka brokers need have public network address, or they are in the same VNET with KAP. With this pre-requisite matched, you can follow the “Streaming Cube” tutorial on KAP manual to create the cube.

Create Model and Cube

With all necessary tables be imported, you can start to define your data model. The model is a basis for Cube, and it can be reused by multiple Cubes. In the “Models” tab, click “+Model”, select fact table and lookup table, select dimension/measure column, and set the model partition information, etc.

Figure 12. Create Data Model

When model is saved, you can create a Cube. Cube is a data structure which has dimensions and measures. KAP supports tens of dimensions in a Cube, and you can define the most common measures like SUM, COUNT, MAX, MIN, DISTINCT COUNT and some others. Click the “+Cube”, a wizard will guide you to finish these step by step.

Figure 13. Create Cube

Build Cube

After the Cube be created, you need build data into it. In the Cube list, click “Action” -> “Build”. If your data model is partitioned, you need specify a date range for the source data; otherwise it will build on full data set.

Figure 14. Build Cube

When triggering a Cube build, a job is created and you can monitor the progress in the “Monitor” page. The build can take a couple of minutes to several hours, depending on your data size and cluster capacity. You can expand the job to see each steps. If a step is a MR job, it shows the link to job on Hadoop resource manager, where you can track the detail progress. Once the build is finished, the Cube status will be changed to “Ready” automatically, which means it can serve your queries.

Run Queries

Click the “Insight” tab, you can compose a SQL query and then click “Submit”; The query will be served with Cube; and you can preview the result in the web page.

Figure 15. Run Queries in KAP web

 

Use KyAnalyzer for Agile BI

Login KyAnalyzer with the same user as KAP, in the “Admin Console” page, click “Sync Cubes from Kylin”, select the “kylin_sales_cube” to sync. The Cube model will be imported to KyAnalyzer.

Figure 16. Sync Cube to KyAnalyzer

Click the “New query” to open a new page, click the refresh button beside “Cubes” to see the new loaded model, select “kylin_sales” in the dropdown list. Now you see the measures and dimensions. Click one measure and one dimension, it will query the data and generate a data table.

Figure 17. Select dimension and measure in KyAnalyzer

Click the “Chart Mode” icon in the upper right corner, KyAnalyzer will generate diagram based on the table, as below shows. Isn’t it easy?

Figure 18. Generate chart in KyAnalyzer

Congratulations! You have built your first Cube and created the first chart with drag and drop. You can also integrate KAP with more visualization tools like Tableau, PowerBI/Excel.

Connect from Tableau, PowerBI

To connect from Tableau or PowerBI, you need install the Apache Kylin ODBC driver (only Windows is supported). The detail guide can be found in http://docs.kyligence.io/v2.3/en/integration/

KAP on HDInsight works with HTTPS protocol on 443 port, so when configuring the ODBC connection, please make sure using the correct URL and port. Figure 18 is a sample from Tableau.

Figure 19. Connect from Tableau to KAP

 

Kafka on HDInsight Integration

Apache Kafka is a popular open-source stream-ingestion broker. It can handle large numbers of reads and writes per second from thousands of clients. KAP supports using Apache Kafka (v0.10+) as the data source. You can flow your data to Kafka topics continuously, and then build data into Cube with incremental build jobs. With this integration, it can decrease the data latency from days/hours to minutes level.

Kafka on HDInsight provides you with a managed, highly scalable, and highly available service in the Azure cloud. You can create a Kafka cluster from Azure portal quickly. To ensure the Kafka version in KAP node is compatible with the Kafka cluster, suggest you use the same HDInsight version. Otherwise, you need manually download and install the matched version of Kafka in KAP instance.

HDInsight does not allow direct connection to Kafka over the public internet. In order to connect with Kafka from KAP, you need make these two clusters share the same VNET, or use VPN solution. The former should be the simplest way, you can use it for a new cluster.

By default, KAP doesn’t have Kafka client shipped; So you will get a “ClassNotFoundException” error when connect to a Kafka cluster. To fix this, you need SSH into the KAP node, manually specify KAFKA_HOME environment variable in KYLIN_HOME/bin/find-kafka-dependency.sh:

ssh sshuser@KAP.CLUSTERNAME-ssh.azurehdinsight.net sudo vi /usr/local/kap/bin/setenv.sh

In the beginning position, add (remember to update the HDP version):

export KAFKA_HOME=/usr/hdp/2.5.5.3-2/kafka

Then you can verify whether it can find the dependent jar successfully, like this:

sshuser@ed10-kapdem:~$ /usr/local/kap/bin/find-kafka-dependency.sh -v
Turn on verbose mode.Retrieving kafka dependency...KAFKA_HOME is set to: /usr/hdp/2.5.5.3-2/kafka, use it to locate kafka dependencies.kafka dependency: /usr/hdp/2.5.5.3-2/kafka/libs/kafka-clients-0.10.0.2.5.5.3-2.jar

If OK, then restart KAP service to take effective:

sudo systemctl restart kap

When KAP is restarted, go back to the model page, click “Add Streaming Table” button, enter the Kafka broker info, it will connect to it,  list the topics and then show sample messages, as Figure 19 shows.

Figure 20. Kafka Integration

For the next steps, just follow the “Streaming Cube” section in KAP manual.

Best Practices of KAP on HDInsight

Here are some best practices that can help you in using this solution better.

1.Use dedicated virtual network for HDInsight

If not specified, Azure will use a default virtual network for each HDInsight cluster. The default virtual network is invisible in Azure portal; so once the cluster be created, you couldn’t add other instances to it nor customize its network firewall. In order to keep the flexibility for future, create a dedicated virtual network and then use it for the new HDInsight is recommended. The virtual network need be associated with a network security group, which need allow the connections for HDInsight required hosts and ports. You can check HDInsight documentation for the details.

2.Optimize Hadoop settings

The default Hadoop settings of HDInsight might not be the best for KAP, it may allocate much resources to HBase but leave less for MapReduce. Besides, as the Cubes are immutable in HBase, we can allocate more resource to block cache to gain better read performance. You can optimize these settings in the HDInsight dashboard (Ambari Web UI). Here are some recommended settings:

After update these parameters, remember to sync the changes to cluster nodes and then restart the services to take effective.

3.Use multiple storage accounts

Using multiple storage accounts will better leverage the network bandwidth, so to improve the performance. You can use separated storage accounts for the source Hive tables.

4.Scale and shrink on demand

With data in Azure Blob Store or Azure Data Lake, you can easily scale and shrink the cluster without worry about data loss. For example, when you build a big Cube, you can scale out it to get more concurrencies; After the build is finished, you can shrink it back to save the cost.

5.Shutdown and bring back

KAP uses HBase to store the Cube metadata and Cube data. With the Hadoop/HBase data persisted in Azure Blob Store or Azure Data Lake at a very low cost, you can safely delete the cluster when you don’t need it, and someday when you need, bring it back by re-creating the cluster with the same storage account and container. The Cubes that you built previously will be working for you as before. This will be great if it is a low frequent usage scenario.

While there are some other data you may need take a manual backup:

a).KAP/KyAnalyzer configurations and logs

KAP configurations (as well as shell scripts) are local files on the edge node. If you have made some local changes and want to keep after re-creation, a backup is needed.

b).Hive metadata

Hive metadata is not persisted in Azure Blob Store, re-create Cluster may lose the Hive metadata. So you’d better backup the Hive scripts for future. Or use an Azure SQL instance as Hive metadata store when creating HDInsight.

6.Start/stop KAP and KyAnalyzer

  • KAP and KyAnalyzer are installed in the HDInsight edge node. You need SSH into it with the ssh user and credential that configured for the cluster:
ssh sshuser@KAP.CLUSTERNAME-ssh.azurehdinsight.net

Replace the “CLUSTERNAME” with your cluster name.

  • KAP and KyAnalyzer are installed in “/usr/local/kap” (KYLIN_HOME) and “/usr/local/kyanalyzer” (KYANALYZER_HOME). You can check the scripts, configurations and log files there. e.g:
tail -200f /usr/local/kap/logs/kylin.logtail -200f /usr/local/kyanalyzer/tomcat/logs/kyanalyzer.log
  • KAP and KyAnalyzer are registered as system services. You can manually start, stop them with systemctl command. For example:
sudo systemctl status kapsudo systemctl stop kap sudo systemctl start kap

For KyAnalyzer, change to “kyanalyzer” as the service name.

Frequent Asked Questions (FAQ)

Below are some FAQ about KAP on HDInsight solution.

1.Where is the online documentation?

Kyligence user manual is the main entry of documentation: http://manual.kyligence.io/; Besides, Apache Kylin document is a supplement: https://kylin.apache.org/docs20/

2.How to get help?

When get a question or problem, do a search is the first step. You can use “KAP” as well as “Apache Kylin” as the keyword because KAP uses Kylin as the core, Kylin has been adopted by many users, so there are many discussions, Q&A in public.

If there is not matched post, you can use Kyligence online diagnosis service called “Kybot”, https://kybot.io/. You need register an account to login, and then upload a diagnosis package. The service will analysis the logs and then give advices. There is also a knowledge base in the “Help Center”, contributed by KAP team, you can browse that and search the interested topic.

If all above doesn’t answer your question, or you want to get professional service, contact us at info@kyligence.io

3.How to purchase a commercial license after free trial?

Contact info@kyligence.io, we will have sales representative get back to you.

Thanks for your reading, we’re looking forward to get your feedback soon.

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