During this week's Cassandra Lunch we covered packaged and DIY methods for Lucene-based indexes on Cassandra; as well as some pros and cons for using Lucene Based Indexes on Cassandra. Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-23-lucene-based-indexes-on-cassandra/ Cassandra Lunch Recording: https://youtu.be/Z0NXWmZAB8s Additional Resources: https://docs.datastax.com/en/dse/6.0/dse-dev/datastax_enterprise/search/searchTOC.html https://github.com/instaclustr/cassandra-lucene-index https://github.com/strapdata/elassandra https://github.com/tjake/Solandra Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/ Awesome Cassandra: https://github.com/Anant/awesome-cassandra Follow Us and Reach Us At: Anant: https://www.anant.us/ Cassandra.Link: https://cassandra.link/ Email: solutions@anant.us LinkedIn: https://www.linkedin.com/company/anant/ Twitter: https://twitter.com/anantcorp Eventbrite: https://www.eventbrite.com/o/anant-1072927283 Facebook: https://www.facebook.com/AnantCorp/

1. Version 1.0
Lucene Based Indexes on Cassandra
An Anant Corporation Story.

2. Types Covered + Pros and Cons
● Packaged
● DIY
● Pros
● Cons

3. Packaged

4. DSE Search / Solr
● https://docs.datastax.com/en/dse/6.0/dse-dev/datastax_enterprise/search
/searchTOC.html

5. Cassandra Lucene Index
● instaclustr/cassandra-lucene-index: Lucene based secondary indexes for
Cassandra
a. https://github.com/instaclustr/cassandra-lucene-index
● Mutations to Cassandra -> Mutations on Disk (for that node)

6. Elessandra / ElasticSearch
● strapdata/elassandra: Elassandra = Elasticsearch + Apache Cassandra
a. https://github.com/strapdata/elassandra
● Cassandra + Elasticsearch
a. Mutations to Cassandra -> Mutations to Elasticsearch

7. DIY

8. Event -> CQRS -> Cassandra + Index
● Write
○ Event / Command goes into an Event Source repository (Kafka, SQL
Table, etc. )
○ Command Processor processes it into CQL / Elasticsearch or SOLR or
Amazon ... Algolia
● Request
○ Event / Command goes into an Event Source repository (Kafka, SQL
Table, etc. )
○ Command Processor goes to index / finds the data, goes to Cassandra ,
gets the data, returns.
○ Query the index --

9. Cassandra -> Batch -> Index
● Writes to Cassandra
● Every now and then - Index to ???
● Cassandra + Spark

10. Cassandra Triggers -> Index

11. Serverless Function -> Cassandra + Index

12. Apache Nifi (Lucene) -> Nifi Processor -> Cassandra + Index

13. Pros
● extremely rich search capabilities
● Geospatial
● synonym
● fuzzy logic search
● Typos
● Stemming
● packaged elastic/solr/lucene -> shorter latency
● separate index -> better separation of concerns and speed

14. Cons
● pure lucene -> reinventing the wheel of what Solr/ElasticSearch
● external elastic/solr/ ?? -> longer latency between finding the data / getting
the data
● packaged elastic/solr/lucene -> don't expect it to solve all your problems
● consistency issues (if DIY)
● lucene is memory heavy
● lucene is disk heavy

15. Strategy: Scalable Fast Data
Architecture: Cassandra, Spark, Kafka
Engineering: Node, Python, JVM,CLR
Operations: Cloud, Container
Rescue: Downtime!! I need help.
www.anant.us | solutions@anant.us | (855) 262-6826
3 Washington Circle, NW | Suite 301 | Washington, DC 20037

16. Resources
● tjake/Solandra: Solandra = Solr + Cassandra