This document discusses Cassandra and techniques for inserting data into Cassandra using the Cassandra driver. It describes three methods for inserting data - execute (blocks until response), execute async (returns immediately without blocking), and batch insert (combines multiple statements). It also covers pagination in Cassandra using fetch size, saving the paging state, and offset queries. Performance comparisons show execute async has lower execution time than execute/sync for the same number of entries.

1. Cassandra – Tips And Techniques
Piyush Rana
Software Consultant
Knoldus Software LLP

2. Agenda
1) What is Cassandra ?
2) Ways to Insert Data in Cassandra Using Cassandra
Driver?
3) Pagination?

3. What Is Cassandra
The Apache Cassandra database is the right choice when you need
scalability and high availability without compromising performance.
● Linear scalability and proven fault-tolerance on commodity
hardware or cloud infrastructure make it the perfect platform for
mission-critical data

4. Inserts In Cassandra
Execute Method
● This method blocks until at least some result has been received from the
database.
● But it does guarantee that some response has been received from the
database, and in particular guarantees that if the request is invalid, an
exception will be thrown by this method.
● Commonly Used method , Use When Immediate Confirmation of data
Insertion is required .

5. Inserts In Cassandra
Execute Async Method
● This method does not block. It returns as soon as the query has been
passed to the underlying network stack.
● Any exception pertaining to the failure of the
query will be thrown when accessing the {@link ResultSetFuture}.
● The Java driver allows you to take advantage of its non-blocking nature through its
execute Async method. Guava and Rx-Java provide powerful combinators to transform
and compose these asynchronous results.

6. Inserts In Cassandra
Execute Async Method

7. Inserts In Cassandra
Batch Insert Method
● A BATCH statement combines multiple data modification language (DML)
statements into a single logical operation.
● Batching multiple statements can save network exchanges between the
client/server and server coordinator/replicas.
● Has a Comparative execution as Execute Asyn , But restricts in size
constraints .
● Use When Something like transaction are required .

8. Comparison
Enteries Execute/Sync (sec) Async (ms)
5000 8.696759609 1.095662452
10000 11.79588774 1.367923948
20000 15.98167063 1.875919844
40000 24.02840491 3.4962987
100000 34.51174474 7.775452692
Performance on 3 nodes Cluster - Simple Strategy - Replication Factor = 2 -
Consistency Level = 1

9. PAGINATION
1) Setting the fetch size
2) Saving and reusing the paging state
3) Offset queries

10. Setting the fetch size
The fetch size specifies how many rows will be returned at
once by Cassandra (in other words, it’s the size of each page).

11. Saving and reusing the paging state
Sometimes it is convenient to save the paging state in order to
restore it later.
● For example, consider a stateless web service that displays a
list of results with a link to the next page. When the user clicks
that link, we want to run the exact same query, except that the
iteration should start where we stopped on the previous page.

12. Offset queries
● For example, if the page size is 10, the fetch size is 50, and the user
asks for page 12 (rows 110 to 119):
● execute the statement a first time (the result set contains rows 0 to
49, but you’re not going to use them, only the paging state);
● execute the statement a second time with the paging state from the
first query;
● execute the statement a third time with the paging state from the
second query. The result set now contains rows 100 to 149;
skip the first 10 rows of the iterator. Read the next 10 rows and
discard the remaining ones.

13. Thank you !