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Advice on Apache Solr and Cassandra
Vinay Mehta
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CassandraCassandra
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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
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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Recommends
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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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.

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Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 86.3K views
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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 Solr
Pros of Cassandra
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    • 115
      Distributed
    • 96
      High performance
    • 80
      High availability
    • 74
      Easy scalability
    • 52
      Replication
    • 26
      Reliable
    • 26
      Multi datacenter deployments
    • 9
      OLTP
    • 7
      Open source
    • 7
      Schema optional
    • 2
      Workload separation (via MDC)
    • 1
      Fast

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    Cons of Apache Solr
    Cons of Cassandra
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      • 3
        Reliability of replication
      • 1
        Size
      • 1
        Updates

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      - No public GitHub repository available -

      What is Apache Solr?

      It uses the tools you use to make application building a snap. It is built on the battle-tested Apache Zookeeper, it makes it easy to scale up and down.

      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 are some alternatives to Apache Solr and Cassandra?
      Splunk
      It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
      Lucene
      Lucene Core, our flagship sub-project, provides Java-based indexing and search technology, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities.
      Elasticsearch
      Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
      MongoDB
      MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
      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.
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