datastax

I tried reading up on datastax blogs and documentation but could not find any specific on this Is there a way to keep 2 tables in Cassandra to belong to same partition? For example: CREATE TYPE addr ( street_address1 text, city text, state text, country text, zip_code text, ); CREATE TABLE foo ( account_id timeuuid, data text, site_id int, PRIMARY KEY (account_id) }; CREATE TABLE bar ( account_id timeuuid, address_id int, address frozen<addr>, PRIMARY KEY (account_id, address_id) ); Here I need to ensure that both of these tables/CF will live on same partition that way for the same account_id

When I issue $ nodetool compactionhistory I get . . . compacted_at bytes_in bytes_out rows_merged . . . 1404936947592 8096 7211 {1:3, 3:1} What does {1:3, 3:1} mean? The only documentation I can find is this which states the number of partitions merged which does not explain why multiple values and what the colon means. So basically it means {tables:rows} for example {1:3, 3:1} means 3 rows were taken from one sstable (1:3) and 1 row taken from 3 (3:1) sstables, all to make the one sstable in that compaction operation. I tried it out myself so here's an example, I hope this helps: create

cassandra cql shell window got disappears after installation in windows? this was installed using MSI installer availalbe in planet cassandra. Why this happens ? please help me.. Thanks in advance. I had the same issue with DataStax 3.9. This is how I sorted this: Step 1: Open file: DataStax-DDC\apache-cassandra\conf\cassandra.yaml Step 2: Uncomment the cdc_raw_directory and set new value to (for windows) cdc_raw_directory: "C:/Program Files/DataStax-DDC/data/cdc_raw" Step 3: Goto Windows Services and Start the DataStax DDC Server 3.9.0 Service I had the same problem with DataStax Community 3

Theoretically, Cassandra allows up to 2 billion columns in a wide row. I have heard that in reality up to 50.000 cols/50 MB are fine; 50.000-100.000 cols/100 MB are OK but require some tuning; and that one should never go above 100.000/100 MB columns per row. The reason being that this will put pressure on the heap. Is there some truth to this? In Cassandra, the maximum number of cells (rows x columns) in a single partition is 2 billion . Additionally, a single column value may not be larger than 2GB, but in practice, "single digits of MB" is a more reasonable limit, since there is no

I have 64 spark cores. I have over 80 Million rows of data which amount to 4.2 GB in my cassandra cluster. I now need 82 seconds to process this data. I want this reduced to 8 seconds. Any thoughts on this? Is this even possible? Thanks. This is the part of my spark app I want to improve: axes = sqlContext.read.format("org.apache.spark.sql.cassandra")\ .options(table="axes", keyspace=source, numPartitions="192").load()\ .repartition(64*3)\ .reduceByKey(lambda x,y:x+y,52)\ .map(lambda x:(x.article,[Row(article=x.article,at=x.at,comments=x.comments,likes=x.likes,reads=x.reads,shares=x.shares)]))

I am using below datastax versions with java8 <dependency> <groupId>com.datastax.cassandra</groupId> <artifactId>cassandra-driver-core</artifactId> <version>3.7.2</version> </dependency> <dependency> <groupId>com.datastax.cassandra</groupId> <artifactId>cassandra-driver-mapping</artifactId> <version>3.7.2</version> </dependency> My table has a Date column as below cass_table ( data_source_id int, company_id text, create_date date) When I trying to save the data into C* table as below final IndustryCompany four = new IndustryCompany(1,1236, ProdUtils.today()); industryCompanyRepository.save(one

I have test case in which I receive 150k requests per second from a client. My test case requires inserting UNLOGGED batch to multiple tables and having different partition keys BEGIN UNLOGGED BATCH update kspace.count_table set counter=counter+1 where source_id= 1 and name='source_name' and pname='Country' and ptype='text' and date='2017-03-20' and pvalue=textAsBlob('US') update kspace.count_table set counter=counter+1 where source_id= 1 and name='source_name' and pname='City' and ptype='text' and date='2017-03-20' and pvalue=textAsBlob('Dallas') update kspace.count_table set counter=counter