Thanks for your response. What do you mean when you said "immediately
return"?
On Wed, Mar 28, 2018, 10:33 PM Jörn Franke wrote:
> I don’t think select * is a good benchmark. You should do a more complex
> operation, otherwise optimizes might see that you don’t do anything in the
> query and im
I don’t think select * is a good benchmark. You should do a more complex
operation, otherwise optimizes might see that you don’t do anything in the
query and immediately return (similarly count might immediately return by using
some statistics).
> On 29. Mar 2018, at 02:03, Tin Vu wrote:
>
>
Hi Team,
I am new to Spark, my requirement is I have a huge list, which is converted
to spark dataset and I need to operate on this dataset and store computed
values in another object/dataset and store in memory for further processing.
Approach I tried is : list is retrieved from third party in
-
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val test_schema = DataType.fromJson(schema).asInstanceOf[StructType]
val session = SparkHelper.getSparkSession
val df1: DataFrame = session.read
.format("json")
.schema(test_schema)
.option("inferSchema","false")
.option("mode","FAILFAST")
.load("src/test/resources/*.gz")
df1.show(80)
On
I've had more success exporting the schema toJson and importing that.
Something like:
val df1: DataFrame = session.read
.format("json")
.schema(test_schema)
.option("inferSchema","false")
.option("mode","FAILFAST")
.load("src/test/resources/*.gz")
df1.show(80)
On Wed, Mar 28, 2018 at
Hi,
I am executing a benchmark to compare performance of SparkSQL, Apache Drill
and Presto. My experimental setup:
- TPCDS dataset with scale factor 100 (size 100GB).
- Spark, Drill, Presto have a same number of workers: 12.
- Each worked has same allocated amount of memory: 4GB.
- Da
-
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The to String representation look like where "someName" is unique:
StructType(StructField("someName",StringType,true),
StructField("someName",StructType(StructField("someName",StructType(StructField("someName",StringType,true),
StructField("someName",StringType,true)),true),
StructField("someName
Hi:
I am using Apache Spark Structured Streaming (2.2.1) to implement custom
sessionization for events. The processing is in two steps:1.
flatMapGroupsWithState (based on user id) - which stores the state of user and
emits events every minute until a expire event is received
2. The next step i
I've been learning spark-sql and have been trying to export and import
some of the generated schemas to edit them. I've been writing the
schemas to strings like df1.schema.toString() and
df.schema.catalogString
But I've been having trouble loading the schemas created. Does anyone
know if it's poss
I suppose that it's hardly possible that this issue is connected with
the string encoding, because
- "pr^?files.10056.10040" should be "profiles.10056.10040" and is
defined as constant in the source code
-
"profiles.total^@^@f2-a733-9304fda722ac^@^@^@^@profiles.10361.10005^@^@^@^@.total^@^@0075^@
Encoding issue of the data? Eg spark uses utf-8 , but source encoding is
different?
> On 28. Mar 2018, at 20:25, Sergey Zhemzhitsky wrote:
>
> Hello guys,
>
> I'm using Spark 2.2.0 and from time to time my job fails printing into
> the log the following errors
>
> scala.MatchError:
> profiles
Hello guys,
I'm using Spark 2.2.0 and from time to time my job fails printing into
the log the following errors
scala.MatchError:
profiles.total^@^@f2-a733-9304fda722ac^@^@^@^@profiles.10361.10005^@^@^@^@.total^@^@0075^@^@^@^@
scala.MatchError: pr^?files.10056.10040 (of class java.lang.String)
sc
Hi:
I am using spark structured streaming 2.2.1 and am using flatMapGroupWithState
and a groupBy count operators.
In the StreamExecution logs I see two enteries for stateOperators
"stateOperators" : [ {
"numRowsTotal" : 1617339,
"numRowsUpdated" : 9647
}, {
"numRowsTotal" : 1326355,
I have been running into this as well, but I am using S3 for checkpointing
so I chalked it up to network partitioning with s3-isnt-hdfs as my storage
location. But it seems that you are indeed using hdfs, so I wonder if there
is another underlying issue.
On Wed, Mar 28, 2018 at 8:21 AM, Jone Zhang
The spark streaming job running for a few days,then fail as below
What is the possible reason?
*18/03/25 07:58:37 ERROR yarn.ApplicationMaster: User class threw
exception: org.apache.spark.SparkException: Job aborted due to stage
failure: Task 16 in stage 80018.0 failed 4 times, most recent failur
Quick comment:
Excel CSV (very special case though) supports arrays in CSV using "\n"
inside quotes, but you have to use as EOL for the row "\r\n" (Windows EOL).
Cheers,
Jiri
2018-03-28 14:14 GMT+02:00 Yong Zhang :
> Your dataframe has array data type, which is NOT supported by CSV. How csv
> f
Your dataframe has array data type, which is NOT supported by CSV. How csv file
can include array or other nest structure?
If you want your data to be human readable text, write out as json in your case
then.
Yong
From: Mina Aslani
Sent: Wednesday, March 28
I'm using spark-unit-test and I don't get to compile the code.
test("Testging") {
val inputInsert = A("data2")
val inputDelete = A("data1")
val outputInsert = B(1)
val outputDelete = C(1)
val input = List(List(inputInsert), List(inputDelete))
val output = (List(List(outp
Hi Michael,
I think that is what I am trying to show here as the documentation mentions
"NOTE: In Spark 1.0 and later this will be overridden by SPARK_LOCAL_DIRS
(Standalone, Mesos) or LOCAL_DIRS (YARN) environment variables set by the
cluster manager."
So, in a way I am supporting your statement
Hi,
this property will be used in YARN mode only by the driver.
Executors will use the properties coming from YARN for storing temporary
files.
Best,
Michael
On Wed, Mar 28, 2018 at 7:37 AM, Gourav Sengupta
wrote:
> Hi,
>
>
> As per documentation in: https://spark.apache.org/
> docs/latest/co
Hi ,
Here is example snippet in scala
// Convert to a Date typeval timestamp2datetype: (Column) => Column =
(x) => { to_date(x) }df = df.withColumn("date",
timestamp2datetype(col("end_date")))
Hope this helps !
Thanks,
Divya
On 28 March 2018 at 15:16, Junfeng Chen wrote:
> I am working on
I am working on adding a date transformed field on existed dataset.
The current dataset contains a column named timestamp in ISO format. I want
to parse this field to joda time type, and then extract the year, month,
day, hour info as new column attaching to original dataset.
I have tried df.withC
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