What are differences between column-based or column-oriented?
Is there any differences for Cassandra about two of them?
Please give example for each of them?
Column-based and column-oriented are essentially the same thing. Essentially, data for specific columns is stored together to make querying that data faster, as well as scalable. Examples of columnar DBMS products are: Druid, MonetDB, and Vertica.
In terms of how Cassandra relates, the answer to that is that it doesn't. Cassandra is a partitioned row-store. Column values are stored by partitions and rows.
You are not alone in this perception, as many people mistake Cassandra for a "columnar" data store. Earlier versions of Cassandra were considered "schemaless," so that may be where some of the confusion originates. But Cassandra has never embraced a storage model which keeps data for specific columns together.
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We are using Cassandra 3 and have come up with a modelling based on the initial requirements. Since there have been very frequent requirements changes, this model has subsequently changed many times as well. Hence considering these requirements and model changes, there has been no major improvement in terms of development. The team have decided to go with the BLOB data type and store the entire data in the BLOB. Can you please share the drawback to use BLOB such a scenario. Thanks in Advance.
We migrated from Astyanax Cassandra 1.1 to CQL Cassandra 3.0 directly, so we still have a lot of column families which have value as BLOB.
Major issues we face right now are:
1) Difficult to visualize data directly from database: Biggest advantage of CQL is it supports SQL like queries, hence logging into cql terminal and getting results directly from there is saves a lot of time normally. If you use BLOB you will not be able to do all such things.
2) CQL performs better when your table has a well defined schema instead of using blob to store big chunk of data together.
If you are creating a new table, I will suggest to use Collections for your use case. You will be able to store different type of data and performance will also be good.
Nice slides comparing performance of schemaless tables and tables with scehma and collections. You can skip to slide 26 if you just want the summary.
https://www.slideshare.net/DataStax/migration-from-thrift-to-cql-brij-bhushan-ravat-ericsson-cassandra-summit-2016
I started reading Cassandra the definitive guide, which is based on Cassandra 0.7. Now, I'm trying to experiment with Cassandra 2.1.5 and it seems that there's a lot of differences which is really confusing.
For example, I see that in 0.7 version CQL did not exist. On the other hand, data model seems quite different. You can now define a schema with CQL, while in version 0.7 there was no schema.
Can anyone shortly explain the differences, especially about the data model?
I understand that in 0.7 version the idea was about different length rows, that is, rows that have different number of columns. But now I understand that each column is actually a field that contains a number of parameters, so you can have as much fields as you want within the same row (same key).
Can someone summarize the differences? Maybe I did not understand correctly.
An important point to consider, is that the underlying storage model remains the same. CQL is simply an abstraction layer on top of that model, to make it easier to work with and model your data. DataStax MVP John Berryman has a great article on this: Understanding How CQL3 Maps to Cassandra’s Internal Data Structure
In this article, Berryman observes that:
The value of the CQL primary key is used internally as the row key (which in the new CQL paradigm is being called a “partition key”).
The names of the non-primary key CQL fields are used internally as columns names. The values of the non-primary key CQL fields are then internally stored as the corresponding column values.
Additionally, he outlines the benefits of using the CQL-based approach:
It provides fast look-up by partition key and efficient scans and slices by cluster key.
It groups together related data as CQL rows. This means that you can do in one query what would otherwise take multiple queries into different column families.
It allows for individual fields to be added, modified, and deleted independently.
It is strictly better than the old Cassandra paradigm. Proof: you can coerce CQL Tables to behave exactly like old-style Cassandra ColumnFamilies. (See the examples here.)
It extends easily to implementation of sets lists and maps (which are super ugly if you’re working directly in old cassandra) — but that’s for another blog post.
The CQL protocol allows for asynchronous communication as compared with the synchronous, call-response communication required by Thrift. As a result, CQL is capable of being much faster and less resource intensive than Thrift – especially when using single threaded clients.
can have as much fields as you want within the same row (same key).
Actually, there is a hard limit of about 2 billion columns per partition (rowkey).
I have a question about optimal Cassandra database design: is it efficient to have a single table with a large number of skinny rows or is it efficient to have a keyspace with many many tables?
The context:
I am trying to store data from multiple sensors. One approach would be to have a single table that stores data from all sensors. The other approach would be to have one table per sensor. Which one is better?
Please advise.
I'd go with fewer tables for a number of reasons:
As Andy Tolbert mentioned in his reply, each table introduces some overhead which builds up to a large amount when you have 10s or 100s of thousands of tables. Think of it as increasing your overhead/value ratio
If you are dealing with a large number of tables, chances are you'll be creating some of them dynamically during the application's normal operating time. If that is the case, you may see errors in Cassandra as it can fail to propagate the schemas of some new tables across the cluster when it's under pressure. I've seen this in C* 2.0 but I'm not sure if it's still an issue in the latest versions.
Most of the benefits of a multi-table schema can be gained from putting extra thought into single-table data modelling. Having said that, there are cases when segregating data into discrete tables really is the most appropriate solution. One example of this is in certain multi-tenancy systems where data for different tenants needs to be kept physically separate and backed up in isolation, for regulatory reasons.
It is much better and idiomatic to have 1 table for all sensors. There is some overhead introduced with each table (mxbeans for metrics, files, etc.) so you don't have want to have too many.
When you say 'a large number of skinny rows' I don't anticipate that being a problem, you can have many unique keys/partitions (some crazy large number).
I'm preparing a course on NoSQL for database novices. Did a lot of research online and now, I'm in a dilemma as to categorize Cassandra as a Wide Column Store or a Key Value Store? Or shall I call it a two dimensional Key Value Store? I'm having the same issue with CouchBase. Is it a Key Value store or a Document Store?
I'm looking for a Solid way to categorize NoSQL Databases in their versions in 2015. Any help is appreciated.
Since there is a Couchbase answer I'll jump-in on the Cassandra side. From the Cassandra GitHub page:
Cassandra is a partitioned row store. Rows are organized into tables
with a required primary key.
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.
I can't make an informed comment on Cassandra (although my gut instinct is Wide Column over K/V), but for Couchbase I'd probably say there's a stronger argument for categorising it as a document store, given the map/reduce functionality (through views), and the upcoming N1QL query language. There is a compelling argument for it being a K/V store, also, but I'd say for the purposes of communicating differences in competing NoSQL solutions in an educational course, categorising it as a document store wouldn't be unreasonable.
Couchbase can also act as a distributed cache, however, which may be something you wish to touch on in your course.
we trying to build a data-ware house for our transaction system.
- We make 5000 -6000 transaction per day, they can go > 20,000.
- Each transaction produce a file, size (> 4MB)
we want to have a system, which can make updates to the existing data, consistent and availability, and have good read performance. Infrastructure is not any issue.
Hbase or cassandra or any other ? your help and guidance is highly appreciated.
Many thanks!
Most of newer nosql platform can do what you need in terms of performance - both hbase and cassandra scales horizontally (also Aerospike and others) so performances can be guaranteed if the data-model respect the "product-patterns" for data distribution.
I would not choose the technology in terms of performances.
What I would do is:
a list of different features offered by a bunch of products and then consider the one that, out of the box, best fit my needs
a list of operation I need to do on data and check if I am not going "against" some specific product
While 1 is easily done the 2 need a deep product analysis. For instance you say you need to update existing data -- let's imagine you choose Cassandra and you update very very frequently a column on which you put a secondary index (that, under the hood, creates a lookup table) for searching purpose. Any time you perform an update on this column on the lookup table a deletion and insertion is performed. You can read in this article that performing many deletes in Cassandra is considered an anti-pattern and can lead to problematic situations. This is just an example I did on Cassandra because is the one I know best among nosql products and not to tell you avoid Cassandra.