I have streaming of data coming from SparkStreaming. Which i need to process and finally want to store the data in Cassandra. So, earlier i was trying to use SparkCassandra connector. But it doesn't give the access of SparkStreaming Context object on workers. So, I have to use separate cassandra-scala driver. Hence, i ended up with phantom. Now, my question is i have already defined the column family in the cassnandra. So, how do i do the select and update query from scala.
I have followed these documentation link1 but i don't understand why do we need to give the table definition at client (scala code) side. Why can't we just give Keyspace, ClusterPoints and ColumnFamily and be done with it.
object CustomConnector {
val hosts = Seq("IP1", "IP2")
val Connector = ContactPoints(hosts).keySpace("KEYSPACE_NAME")
}
realTimeAgg.foreachRDD{ x => if (x.toLocalIterator.nonEmpty) {
x.foreachPartition {
How to achieve select/insert in Cassandra table here using phantom
}
This is not yet possible using phantom, we are actively working on phantom-spark to allow you to do this, but at this stage in time this is still a few months away.
In the interim, you will have to rely on the spark cassandra connector and use the non type-safe API to achieve this. It's a more unfortunate setup, but in the very near future this will be resolved.
Related
Use Case and Description
My use case is described more here, but the gist of the issue is:
I am making a custom SMT and want to make sure the Elasticsearch sink connector deserializes incoming records properly, but then after that I don't need any sort of schema at all. Each record has a dynamic amount of fields set, so I don't want to have any makeUpdatedSchema step (e.g., this code) at all. This both keeps code more simple and I would assume improves performance since I don't have to recreate schemas for each record.
What I tried
I tried doing something like the applySchemaless code as shown here even when the record has a schema by returning something like this, with null for schema:
return newRecord(record, null, updatedValue);
However, in runtime it errors out, saying I have an incompatible schema.
Key Question
I might be misunderstanding the role of the schema at this point in the process (is it needed at all once we're in the Elasticsearch sink connector?) or how it works, and if so that would be helpful to know as well. But is there some way to write a custom SMT like this?
Looking at the example for ClickHouseIO for Apache Beam the name of the output table is hard coded:
pipeline
.apply(...)
.apply(
ClickHouseIO.<POJO>write("jdbc:clickhouse:localhost:8123/default", "my_table"));
Is there a way to dynamically route a record to a table based on its content?
I.e. if the record contains table=1, it is routed to my_table_1, table=2 to my_table_2 etc.
Unfortunately the ClickHouseIO is still in development does not support this. The BigQueryIO does support Dynamic Destinations, so it is possible with Beam.
The limitation in the current ClickHouseIO is around transforming data to match the destination table schema. As a workaround, if your destination tables are known at pipeline creation time you could create a ClickHouseIO per table, then use the data to route to the correct instance of the IO.
You might want to file a feature request in the Beam bug tracker for this.
I'm running into Scala,Apache Spark world and I'm trying to understand how to create a "pipeline" that will generate a DataFrame based on the events I receive.
For instance, the idea is that when I receive a specific log/event I have to insert/update a row in the DF.
Let's make a real example.
I would like to create a DataFrame that will represent the state of the users present in my database(postgres,mongo whatever).
When i say state, I mean the current state of the user(ACTIVE,INCOMPLETE,BLOCKED, etc). This states change based on the users activity, so then I will receive logs(JSON) with key "status": "ACTIVE" and so on.
So for example, I'm receiving logs from a Kafka topic.. at some point I receive a log which I'm interested because it defines useful information about the user(the status etc..)
I take this log, and I create a DF with this log in it.
Then I receive the 2nd log, but this one was performed by the same user, so the row needs to be updated(if the status changed of course!) so no new row but update the existing one. Third log, new user, new information so store as a new row in the existing DF.. and so on.
At the end of this process/pipeline, I should have a DF with the information of all the users present in my db and their "status" so then I can say "oh look at that, there are 43 users that are blocked and 13 that are active! Amazing!"
This is the idea.. the process must be in real time.
So far, I've tried this using files not connecting with a kafka topic.
For instance, I've red file as follow:
val DF = mysession.read.json("/FileStore/tables/bm2ube021498209258980/exampleLog_dp_api-fac53.json","/FileStore/tables/zed9y2s11498229410434/exampleLog_dp_api-fac53.json")
which generats a DF with 2 rows with everything inside.
+--------------------+-----------------+------+--------------------+-----+
| _id| _index|_score| _source|_type|
+--------------------+-----------------+------+--------------------+-----+
|AVzO9dqvoaL5S78GvkQU|dp_api-2017.06.22| 1|[2017-06-22T08:40...|DPAPI|
| AVzO9dq5S78GvkQU|dp_api-2017.06.22| 1|[null,null,[Wrapp...|DPAPI|
+--------------------+-----------------+------+--------------------+-----+
in _source there are all the nested things(the status I mentioned is here!).
Then I've selected some useful information like
DF.select("_id", "_source.request.user_ip","_source.request.aw", "_type").show(false)
+--------------------+------------+------------------------------------+-----+
|_id |user_ip |aw |_type|
+--------------------+------------+------------------------------------+-----+
|AVzO9dqvoaL5S78GvkQU|111.11.11.12|285d5034-dfd6-44ad-9fb7-ba06a516cdbf|DPAPI|
|AVzO9dq5S78GvkQU |111.11.11.82|null |DPAPI|
+--------------------+------------+------------------------------------+-----+
again, the idea is to create this DF with the logs arriving from a kafka topic and upsert the log in this DF.
Hope I explained well, I don't want a "code" solution I'd prefer hints or example on how to achieve this result.
Thank you.
As you are looking for resources I would suggest the following:
Have a look at the Spark Streaming Programming Guide (https://spark.apache.org/docs/latest/streaming-programming-guide.html) and the Spark Streaming + Kafka Integration Guide (https://spark.apache.org/docs/2.1.0/streaming-kafka-0-10-integration.html).
Use the Spark Streaming + Kafka Integration Guide for information on how to open a Stream with your Kafka content.
Then have a look at the possible transformations you can perform with Spark Streaming on it in chapter "Transformations on DStreams" in the Spark Streaming Programming Guide
Once you have transformed the stream in a way that you can perform a final operation on it have a look at "Output Operations on DStreams" in the Spark Streaming Programming Guide. I think especially .forEachRDD could be what you are looking for - as you can do an operation (like checking whether a certain key word is in your string and based on this do a database call) for each element of the stream.
I'm working on an application that uses a generic Slick class to make queries based on information (such as url, user, pass, column count, etc) provided in metadata files or property files. As a result, I am unable to hardcode any information about the tables I will be accessing. Thus, I will be using a lot of raw SQL queries within Slick, and then proceeding to filter and paginate through the data using Slick tools.
My question is this:
In the example provided in Slick's documentation:
import slick.driver.H2Driver.api._
val db = Database.forConfig("h2mem1")
val action = sql"select ID, NAME, AGE from PERSON".as[(Int,String,Int)]
db.run(action)
You see that action has .as[(Int, String, Int)] at the end of it, I'm guessing to tell the compiler what to expect. That makes sense. However, what I'd like to do would require me to know that information based on non-source-code. Is there any way to have the rows returned from the query be some sort of List or Array that I could access with dynamic information (such as index numbers). I'd be willing to accept a List[String] for example to make this less of a type headache.
I'll keep working at it, but as a Slick newbie, I was wondering if anyone more experienced than me would have a solution off the top of their head.
I am new to Cassandra and I am having some difficulties fetching data.
I looked into the function:
list<KeySlice> get_range_slices(column_parent, predicate, range, consistency_level)
But, I do not understand what the column_parent is supposed to be.
Anybody any idea?=
Thanx,
Granit
column_parent is basicly used for indicator of ColumnFamily(but in rare cases it can indicate a supercolumn). In java you would put : new ColumnParent("Posts") there. but there should be one more parameter for namespace in get_range_slices query, I guess you are not using thrift but a client api. then you should check your client's documentation.
Edit:
the definition of ColumnParent in cassandra api :
The ColumnParent is the path to the
parent of a particular set of Columns.
It is used when selecting groups of
columns from the same ColumnFamily. In
directory structure terms, imagine
ColumnParent as ColumnPath + '/../'.
Frail is correct, but the real answer is "don't use raw Thrift, use one of the clients from http://wiki.apache.org/cassandra/ClientOptions instead."