Need advice about which tool to choose?Ask the StackShare community!

Apache Kudu

66
238
+ 1
10
Apache Spark

2.8K
3.2K
+ 1
139
Add tool

Kudu vs Apache Spark: What are the differences?

Kudu: Fast Analytics on Fast Data. A columnar storage manager developed for the Hadoop platform. A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data; Apache Spark: Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Kudu and Apache Spark can be primarily classified as "Big Data" tools.

"Realtime Analytics" is the top reason why over 2 developers like Kudu, while over 45 developers mention "Open-source" as the leading cause for choosing Apache Spark.

Kudu and Apache Spark are both open source tools. It seems that Apache Spark with 22.3K GitHub stars and 19.3K forks on GitHub has more adoption than Kudu with 788 GitHub stars and 263 GitHub forks.

Advice on Apache Kudu and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 367.3K views

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

See more
Replies (2)
Recommends
ElasticsearchElasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.

Cons: The load on ES will be higher, due to upsert.

To use Flink:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State

Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.

See more
Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 238.7K views
Recommends
Apache SparkApache Spark

Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Apache Kudu
Pros of Apache Spark
  • 10
    Realtime Analytics
  • 60
    Open-source
  • 48
    Fast and Flexible
  • 8
    Great for distributed SQL like applications
  • 8
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    In memory Computation
  • 2
    Machine learning libratimery, Streaming in real

Sign up to add or upvote prosMake informed product decisions

Cons of Apache Kudu
Cons of Apache Spark
  • 0
    Restart time
  • 3
    Speed

Sign up to add or upvote consMake informed product decisions

What is Apache Kudu?

A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

What is Apache Spark?

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Need advice about which tool to choose?Ask the StackShare community!

Jobs that mention Apache Kudu and Apache Spark as a desired skillset
CBRE
United Kingdom of Great Britain and Northern Ireland England Feltham
CBRE
United States of America Texas Richardson
CBRE
Philippines National Capital Region Makati City
CBRE
United States of America Texas Richardson
What companies use Apache Kudu?
What companies use Apache Spark?
See which teams inside your own company are using Apache Kudu or Apache Spark.
Sign up for StackShare EnterpriseLearn More

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with Apache Kudu?
What tools integrate with Apache Spark?

Sign up to get full access to all the tool integrationsMake informed product decisions

Blog Posts

Mar 24 2021 at 12:57PM

Pinterest

GitJenkinsKafka+7
3
1847
MySQLKafkaApache Spark+6
2
1809
Aug 28 2019 at 3:10AM

Segment

PythonJavaAmazon S3+16
7
2343
What are some alternatives to Apache Kudu and Apache Spark?
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
HBase
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
Apache Impala
Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.
Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Druid
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
See all alternatives