I am trying to select all records from one Cassandra table (~10M records) which should be distributed over 4 nodes using CQL shell, but every time I do that it partitions the output to 1K records Max. So my question is, it is possible to select all records at once as I am trying to see how much time it takes Cassandra to retrieve all records.
When you write "SELECT * from CF" CQL client will never select everything at once. It's just a stupid action for large data. Instead it will load only first page and give you an iterator. Cassandra from 2.0 version supports automatic query paging. So you should call your select all query and ITERATE over pages to load full column family. See an example for python client. There is no way to load all in one action in CQL now and it shouldn't be.
While it was already pointed out that it's a bad idea to try and load all data in cqlsh, what you're trying to do is still somewhat possible. You just need to set a limit and probably increase the timeout for cqlsh.
user#host:~# cqlsh --request-timeout=600
This will start the shell with a request timeout of 10 minutes.
select * from some_table limit 10000000;
Please do not use this in a production environment, as it might have terrible implications for performance and cluster availability!
Related
Is there any best way that we can get the total number of rows from the Cassandra table?
Regards,
Mani
DatastaxBulk is probably the easiest to install and run.
Apache Spark Cassandra connector could be handy. Once the dataframe is loaded with sc.cassandraTable() you can count
Avoid counting in your code, it does not scale as it performs a full scan of the cluster, the response time will be in seconds.
Avoid counting with CQL select count(*) as you will likely hit the timeout quickly.
you can simply use Count(*) to get row numbers from the table.
For example,
Syntax:
SELECT Count(*)
FROM tablename;
and the expected output looks like this,
count
-------
4
(1 rows)
Background
Cassandra has a built-in CQL function COUNT() which counts the number of rows returned by a query. If you execute an unbounded query (no filter or WHERE clause), it will retrieve all the partitions in the table which you can count, for example:
SELECT COUNT(*) FROM table_name;
Pitfalls
However, this is NOT recommended since it requires a full table scan that would query every single node which is very expensive and will affect the performance of the cluster.
It might work for very small clusters (for example, 1 to 3 nodes) with very small datasets (for example, a few thousand partitions) but in practice it would likely timeout and not return results. I've explained in detail why you shouldn't do this in Why COUNT() is bad in Cassandra.
Recommended solution
There are different techniques for counting records in the database but the easiest way is to use the DataStax Bulk Loader (DSBulk). It is open-source so it's free to use. It was originally designed for bulk-loading data to and exporting data from a Cassandra cluster as a scalable solution for the cqlsh COPY command.
DSBulk has a count command that provides the same functionality as the CQL COUNT() function but has optimisations that break up the table scan into small range queries so doesn't suffer from the same problems as brute-force counting.
DSBulk is quite simple to use and only takes a few minutes to setup. First, you need to download the binaries from DataStax Downloads then unpack the tarball. For details, see the DSBulk Installation Instructions.
Once you've got it installed, you can count the partitions in a table with one command:
$ cd path/to/dsbulk_installation
$ bin/dsbulk count -h <node_ip> -k ks_name -t table_name
Here are some references with examples to help you get started quickly:
Docs - Counting data in tables
Blog - Counting records with DSBulk
Blog - DSBulk Intro + Loading data
You can also use cqlsh as an alternate for small tables.
Refer this documentation
https://www.datastax.com/blog/running-count-expensive-cassandra
I am using QueryCassandra processor in NiFi to fetch data from Cassandra but my query is getting timedoutexception. I want to increase the request time out while running the CQL query from the processor. Is there a way to do that or I will have to write a custom processor?
Most probably you're getting an exception because you're performing query on non-partition key - in this case, the query is distributed to the all nodes, and requires to go through all available data, and this is very slow if you have big data set.
In Cassandra queries are fast only when you're performing them on (at least) partition key. If you need to search on non-partition column, then you need to re-model your tables to match your queries. I recommend to take DS220 course on DataStax Academy for better understanding how Cassandra works.
As #Alex ott said, it is not recommended to query on non partition key. If you still want to do so and increase the timeout for the query, just property Max Wait Time to whatever timeout you want.
EDIT:
tl;dr: Apache's timeout wrapper doesn't really let you use the timeout option.
Now that you mentioned that this is a DataStax exception and not java.util.concurrent.TimeoutException, I can tell you that I've looked into QueryCassandra processor's source code and it seems like Apache just wrapped the query function with a Future to achieve a timeout instead of using DataStax's built-in timeout option. This results in a default non-changeable timeout by the DataStax driver. It should be reported to Apache as a bug.
I'm current using DB2 and planning to use cassandra because as i know cassandra have a read performance greater than RDBMS.
May be this is a stupid question but I have experiment that compare read performance between DB2 and Cassandra.
Testing with 5 million records and same table schema.
With query SELECT * FROM customer. DB2 using 25-30s and Cassandra using 40-50s.
But query with where condition SELECT * FROM customer WHERE cusId IN (100,200,300,400,500) DB2 using 2-3s and Cassandra using 3-5ms.
Why Cassandra faster than DB2 with where condition? So i can't prove which database is greater with SELECT * FROM customer right?
FYI.
Cassandra: RF=3 and CL=1 with 3 nodes each node run on 3 computers (VM-Ubuntu)
DB2: Run on windows
Table schema:
cusId int PRIMARY KEY, cusName varchar
If you look at the types of problems that Cassandra is good at solving, then the reasons behind why unbound ("Select All") queries suck become quite apparent.
Cassandra was designed to be a distributed data base. In many Cassandra storage patterns, the number of nodes is greater than the replication factor (I.E., not all nodes contain all of the data). Therefore, limiting the number of network hops becomes essential to modeling high-performing queries. Cassandra performs very well with specific queries (which utilize the partition/clustering key structure), because it can quickly locate the node primarily responsible for the data.
Unbound queries (A.K.A. multi-key queries) incur the extra network time because a coordinator node is required. So one node acts as the coordinator, queries all other nodes, collates data, and returns the result set. Specifying a WHERE clause (with at least a partition key) and while using a "Token Aware" load balancing policy, performs well for two reasons:
A coordinator node is not required.
The node primarily responsible for the range is queried, returning the result set in a single netowrk hop.
tl;dr;
Querying Cassandra with an unbound query, causes it to incur a lot of extra processing and network time that it normally wouldn't have to do, had the query been specified with a WHERE clause.
Even as a troublesome query like a no-condition range query, 40-50s is pretty extreme for C*. Is the coordinator hitting GCs with the coordination? Can you include code used for your test?
When you make a select * vs millions of records, it wont fetch them all at once, it will grab the fetchSize at a time. If your just iterating through this, the iterator will actually block even if you used executeAsync initially. This means that every 10k (default) records it will issue a new query that you will block on. The serialized nature of this will take time just from a network perspective. http://docs.datastax.com/en/developer/java-driver/3.1/manual/async/#async-paging explains how to do it in a non-blocking way. You can use this to to kick off the next page fetch while processing the current which would help.
Decreasing the limit or fetch size could also help, since the coordinator may walk token ranges (parallelism is possible here but its heuristic is not perfect) one at a time until it has read enough. If it has to walk too many nodes to respond it will be slow, this is why empty tables can be very slow to do a select * on, it may serially walk every replica set. With 256 vnodes this can be very bad.
Recently i configured dse on my system for a project. Now i want to use hive to fetch data from cassandra. Everything was fine. It just took below 1 second for select * queries. But the problem is it is taking a constant time of around 30 second for queries that using aggregate functions (I mean whenever map reduce job is launched). I edited mapred-site.xml(also dse-mapred-default.xml :D) based on datastax documentation to tune up hive performance. But unfortunately no change. Please help me
Hive is not meant for faster query processing. Its a data warehouse system which is preferred when you want to process huge amount of data in batch.
If you need faster results I suggest you try hbase/cassandra.
Somewhere I have heard that using multi row selection in cassandra is bad because for each row selection it runs new query, so for example if i want to fetch 1000 rows at once it would be the same as running 1000 separate queries at once, is that true?
And if it is how bad would it be to keep selecting around 50 rows each time page is loaded if say i have 1000 page views in a single minute, would it severely slow cassandra down or not?
P.S I'm using PHPCassa for my project
Yes, running a query for 1000 rows is the same as running 1000 queries (if you use the recommended RandomPartitioner). However, I wouldn't be overly concerned by this. In Cassandra, querying for a row by its key is a very common, very fast operation.
As to your second question, it's difficult to tell ahead of time. Build it and test it. Note that Cassandra does use in memory caching so if you are querying the same rows then they will cache.
We are using Playorm for Cassandra and there is a "findAll" pattern there which provides support to fetch all rows quickly. Visit
https://github.com/deanhiller/playorm/wiki/Support-for-retrieving-many-entities-in-parallel for more details.
1) I have little bit debugged the Cassandra code base and as per my observation to query multiple rows at the same time cassandra has provided the multiget() functionality which is also inherited in phpcassa.
2) Multiget is optimized to to handle the batch request and it saves your network hop.(like for 1k rows there will be 1k round trips, so it definitely reduces the time for 999 round trips)
3) More about multiget() in phpcassa: php cassa multiget()