Do You Really Need SQL to Do It All in Cassandra?
NoSQL database systems are designed for scalability. The down side of that is a primitive key-value data model and, as the name suggest, no support for SQL. It might sound like a serious limitation – how can I “select”, “join”, “group” and “sort” the data? This post explains how all these operations can be quite naturally and efficiently implemented in one of the most famous NoSQL system – Cassandra.
To understand this post you need to know the Cassandra data model. You can find a quick introduction in my previous post. The power of the Cassandra data model is that it extends a basic key-value store with efficient data nesting (via columns and super columns). It means that you can read/update a column (or a super column) without retrieving the whole record. Below I describe how we can exploit data nesting to support various query operations.
Let’s consider a basic example: departments and employees with one-to-many relationships respectively. So we have two column families: Emps and Deps. In Emps employee IDs are used as keys and there are Name, Birthdate, and City columns. In Deps keys are department IDs and the single column is Name.
1) Select
For example: select * from Emps where Birthdate = '25/04/1975'
To support this query we need to add one more column family named Birthdate_Emps in which key is a date and column names are IDs of those employees that were born on the date. The values are not used here and can be an empty byte array (denoted “-”). Every time when a new employee is inserted/deleted into/from Emps we need to update Birthdate_Emps. To execute the query we just need to retrieve all the columns for the key '25/04/1975' from Birthdate_Emps.
Notice that Birthdate_Emps is essentially an index that allows us to execute the query very efficiently. And this index is scalable as it is distributed across Cassandra nodes. You can go even further to speed up the query by redundantly storing information about employees (i.e. employee’s columns from Emps) in Birthdate_Emps. In this case employee IDs becomes names of super columns that contain corresponding employee columns.
2) Join
For example: select * from Emps e, Deps d where e.dep_id = d.dep_id
What does join essentially mean? It constructs records that represent relationship between entities. Such relationships can be easily (and even more naturally) represented via nesting. To do that add column family Dep_Emps in which key is a department ID and column names are IDs of the corresponding employees.
3) Group By
For example: select count(*) from Emps group by City
From implementation viewpoint Group By is very similar to select/indexing described above. You just need to add a column family City_Emps with cities as keys and employee IDs as column names. In this case you will count the number of employees on retrieval. Or you can have a single column named count which value is the pre-calculated number of employees in the city.
4) Order By
To keep data sorted in Cassandra you can use two mechanisms: (a) records can be sorted by keys using OrderPreservingPartitioner with range queries (more on this in Cassandra: RandomPartitioner vs OrderPreservingPartitioner). To keep nested data sorted you can use automatically supported ordering for column names.
To support all these operations we store redundant data optimized for each particular query. It has two implications:
1) You must know queries in advance (i.e. no support for ad-hoc queries). However, typically in Web applications and enterprise OLTP applications queries are well known in advance, few in number, and do not change often. Read Mike Stonebraker convincingly talking about that. BTW, Constraint Tree Schema, described in the latter paper, also exploits nesting to organize data for predefined queries.
2) We shift the burden from querying to updating because what we essentially do is supporting materialized views (i.e. pre-computed results of queries). But it makes a lot of sense in case of using Cassandra as Cassandra is very much optimized for updates (thanks to eventual consistency and “log-structured” storage borrowed from Google BigTable). So we can use fast updates to speed up query execution. Moreover, use-cases typical for social applications are proven to be only scalable with push-on-change model (i.e. preliminary data propagation via updates with simple queries – the approach taken in this post) in comparison with pull-on-demand model (i.e. data are stored normalized and combined by queries on demand – classical relational approach). On push-on-change versus pull-on-demand read WHY ARE FACEBOOK, DIGG, AND TWITTER SO HARD TO SCALE?
Max's Output 
Great post Max.
The examples are very useful and clear. What happens when you need something not so trivial, say that you need to do not just a join on e.dep_id = d.dep_id, but on some other values. What does one do then? Could you please give an example of that, so one can see how one “scales” the modeling further? Thanks.
Otis Gospodnetic
July 16, 2010 at 6:26 pm
Thank you!
I just did not want to make the post too long. Answering your question, the approach seems to scale quite well by combining the above techniques. For example, suppose that you want to query select e.name from Deps d, Emps e where d.dep_id=e.dep_id and dep.located=’Bern’. You can create a column family that maps cities to the IDs of the employees who work in the city. In general the aproach should work fine for ‘equal’ predicates. It is not a perfect solution for ‘range’ predicates as range scan over keys is not quite efficient.
maxgrinev
July 22, 2010 at 10:21 am
Thanks for your posts Max. I’m starting to get a better grasp of how to structure data in Cassandra based on the logical (normalised) data model and the queries that will be performed. However, I’m still not sure about how to structure the data for queries against multiple attributes.
In your example, you created indices for employee birthdays and employee cities. What if I wanted to find all the employees born in 1975 currently in San Francisco? Can such an index be built or would the query need to be performed against the emps column family?
Thanks!
You can build sort of compound index. A key in this index is the concatenation of the year of birth and the city of current location and each record contains all employees born in this year and currently located in this city.
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[…] Similar to indexes, materialized views store data redundantly. So they also can be supported via triggers. Find general guidelines on managing redundant data (indexes and materialized views) in Cassandra in my post “Do You Really Need SQL to Do It All in Cassandra?”. […]
Extending Cassandra with Asynchronous Triggers « Max's Output
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Junior sheikh (@sohails234)
July 7, 2015 at 6:36 am
Hi,
I have a requirement of using Cassandra in my application. In my application there is one table with lot of data and most of my application uses that table. Due to lot of data,performance of the application is decreasing when i use that table is in Oracle.
So, I have decided to use the Cassandra database for that one table and all other tables in oracle. Lot of business logic is dependent on that table.
No my question is, Can I use the Cassandra for a table which has lot of business logic.
I am unable to implement lot of where clauses for Cassandra database.
Is there any supporting tool to use Cassandra in an efficient way?
Please let me know…
i am in urgency..
Thanks in advance
By Mallik
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[…] Do You Really Need SQL to Do It All in Cassandra? […]
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August 19, 2010 at 2:41 am
What about:
select * from Emps where Birthdate >= ’25/04/1975′;
We have not been able to adopt Cassandra because the notion of this query is nearly impossible (without a fixed data set) with the data model. Maybe I am overlooking something, wouldnt be the first time.
As far as duplicating data, what impact does this have on the databases disk usage footprint?
Basically, I am in a situation where I am collecting a decent amount of data, in a structure that can change, but require the ability of some type of dynamic filter like I mentioned up top… Any suggestions would be great, because I really like the idea of switching from MySql to some NoSql solution, because it would solve most of my dynamic schema issues…
Justin Smith
September 27, 2010 at 10:36 pm
Good information Max.
As you explained for where condition of Birthdate, is we are having two columnfamilies (Emps, Birthdates_Emps) in the Keyspace. If so can explain in brief how storage-conf.xml entry for this.
Thanks in Advance.
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September 28, 2010 at 7:22 am
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how can I “select”, “join”, “group” and “sort” the data in cassandra database . Give with examples
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This is, IMHO, the dark side of the story: suppose you need to know who works in a particular department than you have to create a super column family EMP_DEP where
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instead of making a ColumnFamily for “Birthdate_Emps” why not index that column?
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[
{
validation_class: ????,
column_name : birth_date,
index_type : KEYS,
index_name : birth_date
},
];
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September 13, 2012 at 5:05 pm
Max, I have two simple questions on your article. First, at the end of the article you say “To keep data sorted in Cassandra you can use two mechanisms: (a)…”. But there is no (b). What is (b)? Secondly, you do not include an example as with the earlier explanations. Can you offer an example?
Thank you
Sandy Cairns
October 8, 2012 at 3:37 am
I have an additional question. In relational, especially Business Intelligence, it is common to grab some data and do an “order by” “group by”. The “group by” is done on the data you just ordered. Your article (I’m sure for simplicity; your article is very helpful.) illustrates them as unrelated queries. So, I ask then: how can you do the group by and order by together.
The additional question behind this is: how applicable is Cassandra to Business Intelligence querying and reporting. In BI, you might say: give me a breakdown of product” sales and quantity, by customer by product by week. Or, give me Order profitability by customer, order it (i.e., sort it) by the Org Unit that sold the order, but do a “group by” from product to product subgroup to product group”.
Thank you.
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October 8, 2012 at 3:58 am
[…] based on the Key-Value model, Cassandra also supports the concept of columns and super-columns – essentially nested Key-Value pairs – that facilitate the modeling of more complex data […]
The NoSQL Movement: Key-Value Databases | Articles | DATAVERSITY
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How To Retrieve Data From Cassandra? | Zeno
November 19, 2014 at 8:38 am
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January 21, 2015 at 6:08 am
[…] indexes with the performance of log-structured updates, strong support for denormalization and materialized views, and powerful built-in […]
Learn something about Cassandra | Abc company
February 1, 2015 at 8:09 pm
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New Cassandra DB packs two billion columns in one row | IT World Canada News
February 20, 2015 at 9:06 pm
We shift the burden from querying to updating because what we essentially do is supporting materialized views (i.e. pre-computed results of queries). But it makes a lot of sense in case of using Cassandra as Cassandra is very much optimized for updates (thanks to eventual consistency cloud accounting software
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The down side of that is a primitive key-value data model and, as the name suggest, no support for SQL. It might sound like a serious limitation – how can I “select”, “join”, “group” and “sort” the data? This post explains how all these operations can be quite naturally and efficiently implemented in one of the most famous NoSQL system – HCG drops.
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March 23, 2015 at 11:15 am
You must know queries in advance (i.e. no support for ad-hoc queries). However, typically in Web applications and enterprise OLTP applications queries are well known in advance, few in number, and do not change often. Read Mike Stonebraker convincingly talking about that. BTW, Constraint Tree Schema, described in the latter paper, also exploits nesting to organize data for predefined queries. glasses portland
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May 10, 2015 at 3:42 pm
What does join essentially mean? It constructs records that represent relationship between entities. Such relationships can be easily (and even more naturally) represented via nesting. To do that add column family Dep_Emps in which key is a department ID and column names are IDs of the corresponding employees. read more here
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May 29, 2015 at 1:33 am
[…] indexes with the performance of log-structured updates, strong support for denormalization and materialized views, and powerful built-in […]
Apache Cassandra – cassandra-brute.nse – Nmap Script - Meta Thrunks Security Blog
May 30, 2015 at 1:57 am
Notice that Birthdate_Emps is essentially an index that allows us to execute the query very efficiently. And this index is scalable as it is distributed across Cassandra nodes. You can go even further to speed up the query by redundantly storing information about employees (i.e. employee’s columns from Emps) in Birthdate_Emps. In this case employee IDs becomes names of super columns that contain corresponding employee columns. spartagen XT
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[…] indexes with the performance of log-structured updates, strong support for denormalization and materialized views, and powerful built-in […]
Apache Cassandra 3.1! « Another Word For It
December 8, 2015 at 5:32 pm
[…] indexes with the performance of log-structured updates, strong support for denormalization and materialized views, and powerful built-in […]
Big Data Technologies | kumargautam01
December 29, 2015 at 7:34 am
please update your link: https://maxgrinev.com/2010/07/09/a-quick-introduction-to-the-cassandra-data-model/
( I know just change domain to wordpress)
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June 10, 2018 at 9:17 pm
[…] might find this article useful: Do You Really Need SQL to Do It All in Cassandra? Its a bit old but its principles still […]
Cassandra: Inner Join in cassandra CQL - IZZIDB
January 12, 2021 at 5:47 pm