I would like to know how I can use CDC in cassandra. I found that this is already is implemented started from 3.8 version(
https://issues.apache.org/jira/browse/CASSANDRA-8844). Are there any examples of usage?
1. Enable CDC on cassandra.yaml
cdc_enabled (default: false)
Enable or disable CDC operations node-wide.
2. Enabling CDC on a table
CREATE TABLE foo (a int, b text, PRIMARY KEY(a)) WITH cdc=true;
// or
ALTER TABLE foo WITH cdc=true;
3. After memtable flush to disk you can access the row CDC data in $CASSANDRA_HOME/data/cdc_raw
In this folder cassandra store CommitLogSegments.You can check this link Read CommitLogSegments
Read More : https://github.com/apache/cassandra/blob/8b3a60b9a7dbefeecc06bace617279612ec7092d/doc/source/operating/cdc.rst
You can write your own implementation of CommitLogReader, or use this sample implementation.
However, please note that CDC logs are not too much reliable (because of duplicate events and time taken to flush data to CDC), and are subject to format change in future releases.
I work at ScyllaDB which is Cassandra compatible and has CDC support as well - that is simpler to use.
You can specify if you with to get only the delta, pre=image, post-image. Data is stored in a system generated table and can be accessed and read via CQL.
As such:
there is no need to write and deploy code on the cassandra nodes to consume commitlogs (nor is there a need to flush to get them)
deduplication is inherent to the solution.
you can read more in https://docs.scylladb.com/using-scylla/cdc/
Related
I'm very new to the ETL world and I wish to implement Incremental Data Loading with Cassandra 3.7 and Spark. I'm aware that later versions of Cassandra do support CDC, but I can only use Cassandra 3.7. Is there a method through which I can track the changed records only and use spark to load them, thereby performing incremental data loading?
If it can't be done on the cassandra end, any other suggestions are also welcome on the Spark side :)
It's quite a broad topic, and efficient solution will depend on the amount of data in your tables, table structure, how data is inserted/updated, etc. Also, specific solution may depend on the version of Spark available. One downside of Spark-only method is you can't easily detect deletes of the data, without having a complete copy of previous state, so you can generate a diff between 2 states.
In all cases you'll need to perform full table scan to find changed entries, but if your table is organized specifically for this task, you can avoid reading of all data. For example, if you have a table with following structure:
create table test.tbl (
pk int,
ts timestamp,
v1 ...,
v2 ...,
primary key(pk, ts));
then if you do following query:
import org.apache.spark.sql.cassandra._
val data = spark.read.cassandraFormat("tbl", "test").load()
val filtered = data.filter("""ts >= cast('2019-03-10T14:41:34.373+0000' as timestamp)
AND ts <= cast('2019-03-10T19:01:56.316+0000' as timestamp)""")
then Spark Cassandra Connector will push this query down to the Cassandra, and will read only data where ts is in the given time range - you can check this by executing filtered.explain and checking that both time filters are marked with * symbol.
Another way to detect changes is to retrieve the write time from Cassandra, and filter out the changes based on that information. Fetching of writetime is supported in RDD API for all recent versions of SCC, and is supported in the Dataframe API since release of SCC 2.5.0 (requires at least Spark 2.4, although may work with 2.3 as well). After fetching this information, you can apply filters on the data & extract changes. But you need to keep in mind several things:
there is no way to detect deletes using this method
write time information exists only for regular & static columns, but not for columns of primary key
each column may have its own write time value, in case if there was a partial update of the row after insertion
in most versions of Cassandra, call of writetime function will generate error when it's done for collection column (list/map/set), and will/may return null for column with user-defined type
P.S. Even if you had CDC enabled, it's not a trivial task to use it correctly:
you need to de-duplicate changes - you have RF copies of the changes
some changes could be lost, for example, when node was down, and then propagated later, via hints or repairs
TTL isn't easy to handle
...
For CDC you may look for presentations from 2019th DataStax Accelerate conference - there were several talks on that topic.
(Single Node Cluster)I've got a table having 2 columns, one is of 'text' type and the other is a 'blob'. I'm using Datastax's C++ driver to perform read/write requests in Cassandra.
The blob is storing a C++ structure.(Size: 7 KB).
Since I was getting lesser than desirable throughput when using Cassandra alone, I tried adding Ignite on top of Cassandra, in the hope that there will be significant improvement in the performance as now the data will be read from RAM instead of hard disks.
However, it turned out that after adding Ignite, the performance dropped even more(roughly around 50%!).
Read Throughput when using only Cassandra: 21000 rows/second.
Read Throughput with Cassandra + Ignite: 9000 rows/second.
Since, I am storing a C++ structure in Cassandra's Blob, the Ignite API uses serialization/de-serialization while writing/reading the data. Is this the reason, for the drop in the performance(consider the size of the structure i.e. 7K) or is this drop not at all expected and maybe something's wrong in the configuration?
Cassandra: 3.11.2
RHEL: 6.5
Configurations for Ignite are same as given here.
I got significant improvement in Ignite+Cassandra throughput when I used serialization in raw mode. Now the throughput has increased from 9000 rows/second to 23000 rows/second. But still, it's not significantly superior to Cassandra. I'm still hopeful to find some more tweaks which will improve this further.
I've added some more details about the configurations and client code on github.
Looks like you do one get per each key in this benchmark for Ignite and you didn't invoke loadCache before it. In this case, on each get, Ignite will go to Cassandra to get value from it and only after it will store it in the cache. So, I'd recommend invoking loadCache before benchmarking, or, at least, test gets on the same keys, to give an opportunity to Ignite to store keys in the cache. If you think you already have all the data in caches, please share code where you write data to Ignite too.
Also, you invoke "grid.GetCache" in each thread - it won't take a lot of time, but you definitely should avoid such things inside benchmark, when you already measure time.
How to load data from Cassandra to Teradata in real time? Or what are the possibilities that we can use (like some tools) to achieve this?
CDC (change data capture) or Triggers could work depending a on how "realtime" and consistency requirements.
Triggers could end up sending data to teradata that timeout and fail to write. CDC can give you safe output from consistency standpoint but has a slower turn around time (~10 seconds) and is going to give duplicates per replica.
I have gone through Reading from Cassandra using Spark Streaming and through tutorial-1 and tutorial-2 links.
Is it fair to say that Cassandra-Spark integration currently does not provide anything out of the box to continuously get the updates from Cassandra and stream them to other systems like HDFS?
By continuously, I mean getting only those rows in a table which have changed (inserted or updated) since the last fetch by Spark. If there are too many such rows, there should be an option to limit the number of rows and the subsequent spark fetch should begin from where it left off. At-least once guarantee is ok but exactly-once would be a huge welcome.
If its not supported, one way to support it could be to have an auxiliary column updated_time in each cassandra-table that needs to be queried by storm and then use that column for queries. Or an auxiliary table per table that contains ID, timestamp of the rows being changed. Has anyone tried this before?
I don't think Apache Cassandra has this functionality out of the box. Internally [for some period of time] it stores all operations on data in sequential manner, but it's per node and it gets compacted eventually (to save space). Frankly, Cassandra's (as most other DB's) promise is to provide latest view of data (which by itself can be quite tricky in distributed environment), but not full history of how data was changing.
So if you still want to have such info in Cassandra (and process it in Spark), you'll have to do some additional work yourself: design dedicated table(s) (or add synthetic columns), take care of partitioning, save offset to keep track of progress, etc.
Cassandra is ok for time series data, but in your case I would consider just using streaming solution (like Kafka) instead of inventing it.
I agree with what Ralkie stated but wanted to propose one more solution if you're tied to C* with this use case. This solution assumes you have full control over the schema and ingest as well. This is not a streaming solution though it could awkwardly be shoehorned into one.
Have you considered using composite key composed of the timebucket along with a murmur_hash_of_one_or_more_clustering_columns % some_int_designed_limit_row_width? In this way, you could set your timebuckets to 1 minute, 5 minutes, 1 hour, etc depending on how "real-time" you need to analyze/archive your data. The murmur hash based off of one or more of the clustering columns is needed to help located data in the C* cluster (and is a terrible solution if you're often looking up specific clustering columns).
For example, take an IoT use case where sensors report in every minute and have some sensor reading that can be represented as an integer.
create table if not exists iottable {
timebucket bigint,
sensorbucket int,
sensorid varchar,
sensorvalue int,
primary key ((timebucket, sensorbucket), sensorid)
} with caching = 'none'
and compaction = { 'class': 'com.jeffjirsa.cassandra.db.compaction.TimeWindowedCompaction' };
Note the use of TimeWindowedCompaction. I'm not sure what version of C* you're using; but with the 2.x series, I'd stay away from DateTieredCompaction. I cannot speak to how well it performs in 3.x. Any any rate, you should test and benchmark extensively before settling on your schema and compaction strategy.
Also note that this schema could result in hotspotting as it is vulnerable to sensors that report more often than others. Again, not knowing the use case it's hard to provide a perfect solution -- it's just an example. If you don't care about ever reading C* for a specific sensor (or column), you don't have to use a clustering column at all and you can simply use a timeUUID or something random for the murmur hash bucketing.
Regardless of how you decide to partition the data, a schema like this would then allow you to use repartitionByCassandraReplica and joinWithCassandraTable to extract the data written during a given timebucket.
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.