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kandi X-RAY | spark Summary
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QUESTION
I have a dataframe where one column is ; separated strings, e.g. "str1;str2;str3;str4", I also have another static list "strx;stry;strz", the goal is to split the column string value and check if the split array has any intersection with the static list, and keep that row
I tried
...ANSWER
Answered 2021-Jun-15 at 20:34It seems you're mixing up Spark's split
method for Columns with Scala's split
for Strings. Please see example below for how the two different split
methods are used. Method array_intersect
is for intersecting the split Array column with the split element-filter string.
QUESTION
This example has been tested with Spark 2.4.x. Let's consider 2 simple dataframes:
...ANSWER
Answered 2021-Jun-15 at 12:49This seems like a bug introduced by a bug fix in this ticket. The result was wrong for outer joins
.
Hence the need to add a Project
node (packing of the struct) before the Join
node.
However, we end up with this kind of query plan:
QUESTION
I have a following class that reads csv data into Spark's Dataset
. Everything works fine if I just simply read and return the data
.
However, if I apply a MapFunction
to the data
before returning from function, I get
Exception in thread "main" org.apache.spark.SparkException: Task not serializable
Caused by: java.io.NotSerializableException: com.Workflow
.
I know Spark's working and its need to serialize objects for distributed processing, however, I'm NOT using any reference to Workflow
class in my mapping logic. I'm not calling any Workflow
class function in my mapping logic. So why is Spark trying to serialize Workflow
class? Any help will be appreciated.
ANSWER
Answered 2021-Feb-17 at 08:21you could make Workflow implement Serializeble and SparkSession as @transient
QUESTION
I followed the instructions at Structured Streaming + Kafka and built a program that receives data streams sent from kafka as input, when I receive the data stream I want to pass it to SparkSession variable to do some query work with Spark SQL, so I extend the ForeachWriter class again as follows:
...ANSWER
Answered 2021-Jun-15 at 04:42do some query work with Spark SQL
You wouldn't use a ForEachWriter for that
QUESTION
I'm confused why a type that implements comparable
isn't "implicitly comparable", and also why certain syntaxes of sortWith
won't compile at all:
ANSWER
Answered 2021-Jun-11 at 10:35// Works but won't sort eq millis
val records = iter.toArray.sortWith(_.event_time.getTime < _.event_time.getTime)
QUESTION
Given a Spark dataframe with the following columns I am trying to construct an incremental/running count for each id
based on when the contents of the event
column evaluate to True
.
ANSWER
Answered 2021-Jun-14 at 22:51You can use sum
function, casting your event
as an int:
QUESTION
I have sample tests used from scalatest.org site and maven configuration again as mentioned in reference documents on scalatest.org, but whenever I run mvn clean install
it throws the compile time error for scala test(s).
Sharing the pom.xml
below
ANSWER
Answered 2021-Jun-14 at 07:54You are using scalatest
version 2.2.6
:
QUESTION
I am using the following docker-compose image, I got this image from: https://github.com/apache/airflow/blob/main/docs/apache-airflow/start/docker-compose.yaml
...ANSWER
Answered 2021-Jun-14 at 16:35Support for _PIP_ADDITIONAL_REQUIREMENTS
environment variable has not been released yet. It is only supported by the developer/unreleased version of the docker image. It is planned that this feature will be available in Airflow 2.1.1. For more information, see: Adding extra requirements for build and runtime of the PROD image.
For the older version, you should build a new image and set this image in the docker-compose.yaml
. To do this, you need to follow a few steps.
- Create a new
Dockerfile
with the following content:
QUESTION
I am trying to write a unit test code for my Spark-Scala notebook using scalatest.funsuite but the notebook with test() is not getting executed in databricks. Could you please let me know how can I run it?
Here is the sample test code for the same.
...ANSWER
Answered 2021-Jun-14 at 15:42You need to explicitly create the object for that test suite & execute it. In IDE you're relying on specific runner, but it doesn't work in the notebook environment.
You can use either the .execute
function of create object (docs):
QUESTION
When reading, Spark have a mapping 1:1 to kafka partitions, so, with more partitions we can leverage more parellelism to our job.
But does it apply when Spark is writing in kafka ? Writing the same dataset in one topic with 4 partitions is more fast than writing in a topic with 1 partition ?
...ANSWER
Answered 2021-Jun-14 at 14:31Yes.
If your topic has 1 partition means it is in one broker. So, If you increase producer rate for the topic, then that broker becomes busy. But if you have multiple partitions, your Kafka cluster shared those partitions into different brokers and those production rate shared within multiple brokers. So, Writing the same dataset in one topic with 4 partitions is more fast than writing in a topic with 1 partition.
This not only production rate. In Kafka brokers, There is multiple processes like compactions, compressions, segmentations etc... So with number of messages, that work load becomes high. But with multiple partitions in multiple brokers, it will be distributed.
However, you don’t necessarily want to use more partitions than needed because increasing partition count simultaneously increases the number of open server files and leads to increased replication latency.
from kafka documentation
Distribution The partitions of the log are distributed over the servers in the Kafka cluster with each server handling data and requests for a share of the partitions. Each partition is replicated across a configurable number of servers for fault tolerance. Each partition has one server which acts as the "leader" and zero or more servers which act as "followers". The leader handles all read and write requests for the partition while the followers passively replicate the leader. If the leader fails, one of the followers will automatically become the new leader. Each server acts as a leader for some of its partitions and a follower for others so load is well balanced within the cluster.
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