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

Apache Kudu

59
206
+ 1
9
Cassandra

3.2K
3.1K
+ 1
492
Add tool
Advice on Apache Kudu and Cassandra
Vinay Mehta
Needs advice
on
ScyllaScylla
and
CassandraCassandra

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

See more
Replies (4)
Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 57.5K views
Recommends
CassandraCassandra

i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra

See more
Recommends
ScyllaScylla

Scylla can handle 1M/s events with a simple data model quite easily. The api to query is CQL, we have REST api but that's for control/monitoring

See more
Alex Peake
Recommends
CassandraCassandra

Cassandra is quite capable of the task, in a highly available way, given appropriate scaling of the system. Remember that updates are only inserts, and that efficient retrieval is only by key (which can be a complex key). Talking of keys, make sure that the keys are well distributed.

See more
Recommends
ScyllaScylla

By 55M do you mean 55 million entity changes per 2 minutes? It is relatively high, means almost 460k per second. If I had to choose between Scylla or Cassandra, I would opt for Scylla as it is promising better performance for simple operations. However, maybe it would be worth to consider yet another alternative technology. Take into consideration required consistency, reliability and high availability and you may realize that there are more suitable once. Rest API should not be the main driver, because you can always develop the API yourself, if not supported by given technology.

See more
Get Advice from developers at your company using Private StackShare. Sign up for Private StackShare.
Learn More
Pros of Apache Kudu
Pros of Cassandra
  • 9
    Realtime Analytics
  • 114
    Distributed
  • 95
    High performance
  • 80
    High availability
  • 74
    Easy scalability
  • 52
    Replication
  • 26
    Multi datacenter deployments
  • 26
    Reliable
  • 8
    OLTP
  • 7
    Open source
  • 7
    Schema optional
  • 2
    Workload separation (via MDC)
  • 1
    Fast

Sign up to add or upvote prosMake informed product decisions

Cons of Apache Kudu
Cons of Cassandra
  • 0
    Restart time
  • 2
    Reliability of replication
  • 1
    Updates

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 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.

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

What companies use Apache Kudu?
What companies use Cassandra?
See which teams inside your own company are using Apache Kudu or Cassandra.
Sign up for Private StackShareLearn More

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

What tools integrate with Apache Kudu?
What tools integrate with Cassandra?

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

Blog Posts

What are some alternatives to Apache Kudu and Cassandra?
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 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.
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