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Apache Kudu vs Cassandra: What are the differences?

Apache Kudu vs. Cassandra

Apache Kudu and Cassandra are both popular distributed database management systems used for handling big data. However, there are several notable differences between the two.

1. Storage Architecture: Apache Kudu utilizes a columnar storage architecture, which provides fast analytical scans and aggregation queries. On the other hand, Cassandra uses a row-based storage architecture, making it better suited for high write throughput and transactional workloads.

2. Data Model: Cassandra follows a wide-column store data model, where data is organized into rows with multiple columns. It supports flexible schema design and allows for the storage of large amounts of structured and semi-structured data. In contrast, Kudu adopts a table-like, structured data model with strong schema enforcement, making it more appropriate for use cases that require strict data consistency.

3. Consistency Model: Cassandra employs an eventual consistency model, allowing data to be written to multiple replicas with a possibility of data inconsistencies that are resolved over time. On the contrary, Kudu offers strong consistency guarantees, ensuring that all read operations are always consistent with the most recent write.

4. Secondary Indexes: Kudu provides native support for secondary indexes, allowing efficient search operations on multiple columns. On the other hand, Cassandra requires the use of external tools or custom solutions for secondary indexing.

5. Data Compression and Compression: Kudu supports efficient data compression algorithms, enabling reduced storage requirements and improved query performance. Additionally, it provides support for automatic data compaction, which ensures optimal disk space utilization. In contrast, Cassandra does not offer built-in data compression or automatic compaction capabilities.

6. Query Language Support: Cassandra uses its proprietary query language, Cassandra Query Language (CQL), which is similar to SQL but with some differences. Kudu, on the other hand, provides an extensive SQL-like query language, making it easier for users familiar with SQL to work with the database.

In summary, Apache Kudu and Cassandra differ in their storage architecture, data model, consistency model, support for secondary indexes, data compression and compaction, and query language support. These distinctions make each database system suitable for specific use cases and scenarios.

Advice on Apache Kudu and Cassandra
Vinay Mehta
Needs advice
on
CassandraCassandra
and
ScyllaDBScyllaDB

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.

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Replies (4)
Recommends
on
ScyllaDBScyllaDB

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

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Alex Peake
Recommends
on
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.

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Recommends
on
ScyllaDBScyllaDB

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.

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Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 151.4K views
Recommends
on
CassandraCassandra

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

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Pros of Apache Kudu
Pros of Cassandra
  • 10
    Realtime Analytics
  • 119
    Distributed
  • 98
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 53
    Replication
  • 26
    Reliable
  • 26
    Multi datacenter deployments
  • 10
    Schema optional
  • 9
    OLTP
  • 8
    Open source
  • 2
    Workload separation (via MDC)
  • 1
    Fast

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Cons of Apache Kudu
Cons of Cassandra
  • 1
    Restart time
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates

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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.

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What companies use Apache Kudu?
What companies use Cassandra?
See which teams inside your own company are using Apache Kudu or Cassandra.
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What tools integrate with Cassandra?

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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