I have requirement to update existing values in Cassandra 3.0 to Uppercase.
Its for all rows and for a single column of type text
I would need to do this update all the way to production and want the least intrusive/ iterative way of doing it as some of environment will be update by different team. This will be a one time task
Thanks for help,Appreciate your time
Neha
Cassandra Newbie
Cassandra does not offer native support for this kind of work. You will need to look at either writting your own application that reads and re-insert with upercase. For large scale cluster, you will want to look at using spark on cassandra. Spark is often use on top of Cassandra to perform some analytics, or data migration, like you are doing.
Related
i am working on migration tool oracle to cassandra , where I want to maintain a validation table with columns oracle count and cassandra count , so that i can validate the migration job,in cassandra is there any way system maintains the recently executed/inserted query count ? total count of a particular table ? is there anywhere in cassandra system tables does it store? if so what is it ? if not please suggest some way to design validation framework of data migration.
Is there way in cassandra, get the latest query inserted record count and total count of table in any system tables from where we can read the counts instead of executing the count(*) query on the tables ? does cassandra maintains the of the counts anywhere internally ?If so where we can check the meta data of latest inserts i.e which system tables?
Cassandra is distributed system and there is no place where it will collect the counts per tables. You can get some estimates from system.size_estimates, but it will say only paritions count per range, and their sizes.
For such framework as you're asking, you may need to develop custom Spark code (easiest way) that will perform counting of the rows, and other checks. Spark is highly optimized for effective data access and could be more preferable than writing the custom code.
Also, during migration, consider using consistency level greater than ONE to make sure that at least several nodes confirmed writing of the data. Although, it depends on the amount of data & timing requirements for your migration jobs.
I have to change the schema of one of my tables in Cassandra. It's cannot be done by simply using ALTER TABLE command, because there are some changes in primary key.
So the question is: How to do such a migration in the best way?
Using COPY command in cql is not an option in here because dump file can be really huge.
Can I solve this problem by not creating some custom application?
Like Guillaume has suggested in the comment - you can't do this directly in cassandra. Schema altering operations are very limited here. You have to perform such migration manually using one of suggested there tools OR if you have very large tables you can leverage Spark.
Spark can efficiently read data from your nodes, transform them locally and save them back to db. Remember that such migration requires reading whole db content, so might take a while. It might be the most performant solution, however needs some bigger preparation - Spark cluster setup.
In my application all realtime data storing in a cassandra table, I have plan to analyze it using apache spark and put it into different tables which allows faster data fetch, I want to know which design approach I need to apply for it.
Analyze relatime table in a time-frame , then put in to hourly , then analyze later make it to daily , then weekly etc..., Then it is easy to achieve data in a date range. Is my logic is fine or any other approach with cassandra and spark?
I think your approach is good.It is similar to Lambda Architecture designed by Nathan Marz. For more information, follow this link .Hope this will help you.
I'm evaluating spark-cassandra-connector and i'm struggling trying to get a range query on partition key to work.
According to the connector's documentation it seems that's possible to make server-side filtering on partition key using equality or IN operator, but unfortunately, my partition key is a timestamp, so I can not use it.
So I tried using Spark SQL with the following query ('timestamp' is the partition key):
select * from datastore.data where timestamp >= '2013-01-01T00:00:00.000Z' and timestamp < '2013-12-31T00:00:00.000Z'
Although the job spawns 200 tasks, the query is not returning any data.
Also I can assure that there is data to be returned since running the query on cqlsh (doing the appropriate conversion using 'token' function) DOES return data.
I'm using spark 1.1.0 with standalone mode. Cassandra is 2.1.2 and connector version is 'b1.1' branch. Cassandra driver is DataStax 'master' branch.
Cassandra cluster is overlaid on spark cluster with 3 servers with replication factor of 1.
Here is the job's full log
Any clue anyone?
Update: When trying to do server-side filtering based on the partition key (using CassandraRDD.where method) I get the following exception:
Exception in thread "main" java.lang.UnsupportedOperationException: Range predicates on partition key columns (here: timestamp) are not supported in where. Use filter instead.
But unfortunately I don't know what "filter" is...
i think the CassandraRDD error is telling that the query that you are trying to do is not allowed in Cassandra and you have to load all the table in a CassandraRDD and then make a spark filter operation over this CassandraRDD.
So your code (in scala) should something like this:
val cassRDD= sc.cassandraTable("keyspace name", "table name").filter(row=> row.getDate("timestamp")>=DateFormat('2013-01-01T00:00:00.000Z')&&row.getDate("timestamp") < DateFormat('2013-12-31T00:00:00.000Z'))
If you are interested in making this type of queries you might have to take a look to others Cassandra connectors, like the one developed by Stratio
You have several options to get the solution you are looking for.
The most powerful one would be to use Lucene indexes integrated with Cassandra by Stratio, which allows you to search by any indexed field in the server side. Your writing time will be increased but, on the other hand, you will be able to query any time range. You can find further information about Lucene indexes in Cassandra here. This extended version of Cassandra is fully integrated into the deep-spark project so you can take all the advantages of the Lucene indexes in Cassandra through it. I would recommend you to use Lucene indexes when you are executing a restricted query that retrieves a small-medium result set, if you are going to retrieve a big piece of your data set, you should use the third option underneath.
Another approach, depending on how your application works, might be to truncate your timestamp field so you can look for it using an IN operator. The problem is, as far as I know, you can't use the spark-cassandra-connector for that, you should use the direct Cassandra driver which is not integrated with Spark, or you can have a look at the deep-spark project where a new feature allowing this is about to be released very soon. Your query would look something like this:
select * from datastore.data where timestamp IN ('2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', ... , '2013-12-31')
, but, as I said before, I don't know if it fits to your needs since you might not be able to truncate your data and group it by date/time.
The last option you have, but the less efficient, is to bring the full data set to your spark cluster and apply a filter on the RDD.
Disclaimer: I work for Stratio :-) Don't hesitate on contacting us if you need any help.
I hope it helps!
I'm looking for a tool to load CSV into Cassandra. I was hoping to use RazorSQL for this but I've been told that it will be several months out.
What is a good tool?
Thanks
1) If you have all the data to be loaded in place you can try sstableloader(only for cassandra 0.8.x onwards) utility to bulk load the data.For more details see:cassandra bulk loader
2) Cassandra has introduced BulkOutputFormat bulk loading data into cassandra with hadoop job in latest version that is cassandra-1.1.x onwards.
For more details see:Bulkloading to Cassandra with Hadoop
I'm dubious that tool support would help a great deal with this, since a Cassandra schema needs to reflect the queries that you want to run, rather than just being a generic model of your domain.
The built-in bulk loading mechanism for cassandra is via BinaryMemtables: http://wiki.apache.org/cassandra/BinaryMemtable
However, whether you use this or the more usual Thrift interface, you still probably need to manually design a mapping from your CSV into Cassandra ColumnFamilies, taking into account the queries you need to run. A generic mapping from CSV-> Cassandra may not be appropriate since secondary indexes and denormalisation are commonly needed.
For Cassandra 1.1.3 and higher, there is the CQL COPY command that is available for importing (or exporting) data to (or from) a table. According to the documentation, if you are importing less than 2 million rows, roughly, then this is a good option. Is is much easier to use than the sstableloader and less error prone. The sstableloader requires you to create strictly formatted .db files whereas the CQL COPY command accepts a delimited text file. Documenation here:
http://www.datastax.com/docs/1.1/references/cql/COPY
For larger data sets, you should use the sstableloader.http://www.datastax.com/docs/1.1/references/bulkloader. A working example is described here http://www.datastax.com/dev/blog/bulk-loading.