AI News, Real Time Updates in Hadoop with Kudu, Big Data Journey Part 3
Real Time Updates in Hadoop with Kudu, Big Data Journey Part 3
In part 2 we created an ingestion pipeline using Kafka to read data from twitter and obtain a word count of the most popular phrases for a specific hashtag.
For part 3 we’re going to look at ways of storing this data to be reported on: using Kudu for real time reporting and impala for our historical data.
Once complete simply run the following commands: Next we need to uninstall impala - this is because we need to download the impala-kudu client instead in order to interact with data stored in Kudu.
start and replace service name with kudu-master, kudu-tserver, impala-state-store, impala-catalog, impala-server Note when starting impala we use the same service names as before(without kudu in them) however it will start the version of impala that is configured to work with kudu.
Our schema will look as follows: As you can see, the syntax is as expected however we just need to define a few TBLPROPERTIES outlining that we want to store our data using the KuduStorageHandler, our kudu table name is twitter_ngram, our kudu master is running on localhost:7051(default), our key column is our ngram column and importantly for us (seeing as we haven’t configured tablet replication) is that our number of replicas currently 1.
First we will need to find the ip address of our docker container (within your host machine terminal run ifconfig), and the port will be 8051 by default.
The constantly updating data store is going to be really useful to give us the most current picture, especially when we start to build a real-time dashboard.
Storing the historical data can be really useful as we can run large batch processing applications to scan and spot trends over time or even use machine learning to predict what will happen in the future.
We’ve come a long way - we now have a fully industrialised data stream being ingested into a hadoop cluster, that in real time calculates the most popular phrases being used for a specific twitter hashtag every minute.
Using Impala to Query Kudu Tables
The primary key for a Kudu table is a column, or set of columns, that uniquely identifies every row.
You can specify the PRIMARY KEY attribute either inline in a single column definition, or as a separate clause at the end of the column list: When the primary key is a single column, these two forms are equivalent.
column list: The SHOW CREATE TABLE statement always represents the PRIMARY KEY specification as a separate item in the column list: The notion of primary key only applies to Kudu tables.
If an existing row has an incorrect or outdated key column value, delete the old row and insert an entirely new row with the correct primary key.
Guide to Using Apache Kudu and Performance Comparison with HDFS.
The idea behind this article was to document my experience in exploring Apache Kudu, understanding its limitations if any and also running some experiments to compare the performance of Apache Kudu storage against HDFS storage.
In below example script if table movies already exists then Kudu backed table can be created as follows: Limitations when creating a kudu table: Unsupported data-types: When creating a table from an existing hive table if table has VARCHAR(), DECIMAL(), DATE and complex data types(MAP, ARRAY, STRUCT, UNION) then these are not supported in kudu.
When creating kudu table from another existing table where primary key columns are not first — reorder the columns in the select statement in the create table statement.
As the library for SparkKudu is written in Scala, we would have to apply appropriate conversions such as converting JavaSparkContext to a Scala compatible If we have a data frame which we wish to store to kudu we can do so as follows: Limitations when using kudu via spark: Unsupported Datatypes: Some complex datatypes are unsupported by kudu and creating tables using them would through exceptions when loading via Spark.
Here we can see that the queries take much longer time to run on HDFS Comma separated storage as compared to Kudu, with Kudu (16 bucket storage) having runtimes on an average 5 times faster and Kudu (32 bucket storage) performing 7 times better on a avg.
The test was setup similar to the random access above with 1000 operations run in loop and runtimes measured which can be seen in Table 2 below: Just laying down my thoughts about Apache Kudu based on my exploration and experiments.
From the tests I can see that although it does take longer to initially load data into Kudu as compared to HDFS it does give a near equal performance when it comes to running analytical queries and better performance for random access to data.
Overall I can conclude that if the requirement is for a storage which performs as well as HDFS for analytical queries with the additional flexibility of faster random access and RDBMS features such as Updates/Deletes/Inserts, then Kudu could be considered as a potential shortlist.
- On Tuesday, February 18, 2020
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