Hi Experts,
I would like to submit a spark job with configuring additional jar on hdfs,
however the hadoop gives me a warning on skipping remote jar. Although I
can still get my final results on hdfs, I cannot obtain the effect of
additional remote jar. I would appreciate if you can give me some
Please comment in the JIRA/SPIP if you are interested! We can see the community
support for a proposal like this.
From: Pola Yao
Sent: Wednesday, January 23, 2019 8:01 AM
To: Riccardo Ferrari
Cc: Felix Cheung; User
Subject: Re: I have trained a ML model, now
unsubscribe
Hi Imran,
here is my usecase
There is 1K nodes cluster and jobs have performance degradation because of
a single node. It's rather hard to convince Cluster Ops to decommission
node because of "performance degradation". Imagine 10 dev teams chase
single ops team for valid reason (node has problems)
Could be a tangential idea but might help: Why not use queryExecution and
logicalPlan objects that are available when you execute a query using
SparkSession and get a DataFrame back? The Json representation contains
almost all the info that you need and you don't need to go to Hive to get
this
Explain extended or explain would list the plan along with the tables. Not
aware of any statements that explicitly list dependencies or tables
directly.
Regards,
Ramandeep Singh
On Wed, Jan 23, 2019, 11:05 Tomas Bartalos This might help:
>
> show tables;
>
> st 23. 1. 2019 o 10:43 napísal(a):
Hi Xiangrui
+1
It would be fantastic to see this functionality.
Regards
Alistair.
On 2019/01/15 16:52:44, Xiangrui Meng wrote:
> Hi all,>
>
> I want to re-send the previous SPIP on introducing a DataFrame-based graph>
> component to collect more feedback. It supports property graphs,
Serga, can you explain a bit more why you want this ability?
If the node is really bad, wouldn't you want to decomission the NM entirely?
If you've got heterogenous resources, than nodelabels seem like they would
be more appropriate -- and I don't feel great about adding workarounds for
the
looks like i found the solution in case anyone ever encounters a similar
challenge...
df = spark.createDataFrame(
[("a", 1, 0), ("a", 2, 42), ("a", 3, 10), ("b", 4, -1), ("b", 5, -2), ("b",
6, 12)],
("key", "consumerID", "feature")
)
df.show()
schema = StructType([
StructField("ID_1",
Testing response -- there seems to a problem with replies to this thread.
On 2019/01/15 16:52:44, Xiangrui Meng wrote:
> Hi all,>
>
> I want to re-send the previous SPIP on introducing a DataFrame-based graph>
> component to collect more feedback. It supports property graphs, Cypher>
> graph
Check roll up and cube functions in spark sql.
On Wed, 23 Jan 2019 at 10:47 PM, Pierremalliard <
pierre.de-malli...@capgemini.com> wrote:
> Hi,
>
> I am trying to generate a dataframe of all combinations that have a same
> key
> using Pyspark.
>
> example:
>
> (a,1)
> (a,2)
> (a,3)
> (b,1)
>
Hi,
I am trying to generate a dataframe of all combinations that have a same key
using Pyspark.
example:
(a,1)
(a,2)
(a,3)
(b,1)
(b,2)
should return:
(a, 1 , 2)
(a, 1 , 3)
(a, 2, 3)
(b, 1 ,2)
i want to do something like df.groupBy('key').combinations().apply(...)
any suggestions are
In addition to what Rao mentioned, if you are using cloud blob storage such
as AWS S3, you can specify your history location to be an S3 location such
as: `s3://mybucket/path/to/history`
On Wed, Jan 23, 2019 at 12:55 AM Rao, Abhishek (Nokia - IN/Bangalore) <
abhishek@nokia.com> wrote:
> Hi
This might help:
show tables;
st 23. 1. 2019 o 10:43 napísal(a):
> Hi, All,
>
> We need to get all input tables of several SPARK SQL 'select' statements.
>
> We can get those information of Hive SQL statements by using 'explain
> dependency select'.
> But I can't find the equivalent
Hi Riccardo,
Right now, Spark does not support low-latency predictions in Production.
MLeap is an alternative and it's been used in many scenarios. But it's good
to see that Spark Community has decided to provide such support.
On Wed, Jan 23, 2019 at 7:53 AM Riccardo Ferrari wrote:
> Felix,
Felix, thank you very much for the link. Much appreciated.
The attached PDF is very interesting, I found myself evaluating many of the
scenarios described in Q3. It's unfortunate the proposal is not being
worked on, would be great to see that part of the code base.
It is cool to see big players
I have a a Spark Streaming process that consumes records off a Kafka topic,
processes them and sends them to a producer to publish on another topic. I
would like to add a sequence number column that can be used to identify records
that have the same key and be incremented for each duplicate
Hello dear Sir/Madam,
Please add https://meetup.com/Spark-Singapore/ to the page
https://spark.apache.org/community.html
Thanks,
Arseny
+1
Graph analytics is now mainstream, and having Cypher first-class support in
Spark would allow users to deal with highly connected datasets (fraud
detection, epidemiology analysis, genomic analysis, and so on) going beyond
the limits of joins when you must traverse a dataset.
On 2019/01/15
+1
Graph analytics is now mainstream, and having Cypher first-class support in
Spark would allow users to deal with highly connected datasets (fraud
detection, epidemiology analysis, genomic analysis, and so on) going beyond
the limits of joins when you must traverse a dataset.
On 2019/01/15
Hello Beliefer,
I am orchestrating many spark jobs using Airflow and when some of the spark
jobs get started and running and many other would be in accepted mode and
sometimes 1-2 jobs go to failure state if yarn cannot create container
application.
Thanks
On Wed, Jan 23, 2019 at 9:15 AM 大啊
Spark ThriftServer is a spark application that possess thrift server. your code
is a custom spark application.
If you need some custome function beyond Spark ThriftServer, you can make your
spark application contains HiveThriftServer2.
At 2019-01-23 17:53:01, "Soheil Pourbafrani" wrote:
Hello everyone,
I have a spark application processing data iteratively within an RDD until
.isEmpty() is true. Now the loop is sort of like it follows
mainRDD = sc.parallelize(...) //initialize mainRDD
do {
rdd1 = mainRDD.flatMapToPair(advanceState)//advance state of element
rdd2 =
Hi, I want to create a thrift server that has some hive table predefined
and listen on a port for the user query. Here is my code:
val spark = SparkSession.builder()
.config("hive.server2.thrift.port", "1")
.config("spark.sql.hive.thriftServer.singleSession", "true")
Hi, All,
We need to get all input tables of several SPARK SQL 'select' statements.
We can get those information of Hive SQL statements by using 'explain
dependency select'.
But I can't find the equivalent command for SPARK SQL.
Does anyone know how to get this information of a SPARK SQL
Hi Soheil,
You should able to apply some filter transformation. Spark is lazy
evaluated and the actual loading from Cassandra happens only when an action
triggers it. Find more here:
https://spark.apache.org/docs/2.3.2/rdd-programming-guide.html#rdd-operations
The Spark Cassandra supports
Hi Lakshman,
We’ve set these 2 properties to bringup spark history server
spark.history.fs.logDirectory
spark.history.ui.port
We’re writing the logs to HDFS. In order to write logs, we’re setting following
properties while submitting the spark job
spark.eventLog.enabled true
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