Hi,
I tried using the (typed) Dataset API about three years ago. Then
there were limitations with predicate pushdown, overhead serialization
and maybe more things I've forgotten. Ultimately we chose the
Dataframe API as the sweet spot.
Does anyone know of a good overview of the current state of
Holy war is a bit dramatic don't you think? The difference between Scala
and Python will always be very relevant when choosing between Spark and
Pyspark. I wouldn't call it irrelevant to the original question.
br,
molotch
On Sat, 17 Oct 2020 at 16:57, "Yuri Oleynikov (יורי אולייניקוב)" <
I'm sorry you were offended. I'm not an expert in Python and I wasn't
trying to attack you personally. It's just an opinion about what makes a
language better or worse, it's not the single source of truth. You don't
have to take offense. In the end its about context and what you're trying
to
d limit per micro-batch on data source
> (like maxOffsetsPerTrigger) and process all available input as possible.
> (Data sources should migrate to the new API to take effect, but works for
> built-in data sources like file and Kafka.)
>
> 1. https://issues.apache.org/jira/browse/SPARK-30669
>
>
I've always had a question about Trigger.Once that I never got around to
ask or test for myself. If you have a 24/7 stream to a Kafka topic.
Will Trigger.Once get the last offset(s) when it starts and then quit once
it hits this offset(s) or will the job run until no new messages is added
to the
And to answer your question (sorry, read too fast). The string is not in
proper ISO8601. Extended form must be used throughout, ie
2020-04-11T20:40:00-05:00, there's a colon (:) lacking in the UTC offset
info.
br,
Magnus
On Tue, Mar 31, 2020 at 7:11 PM Magnus Nilsson wrote:
> Timesta
Timestamps aren't timezoned. If you parse ISO8601 strings they will be
converted to UTC automatically.
If you parse timestamps without timezone they will converted to the the
timezone the server Spark is running on uses. You can change the timezone
Spark uses with
Been a while but I remember reading on Stack Overflow you can use a UDF as
a join condition to trick catalyst into not reshuffling the partitions, ie
use regular equality on the column you partitioned or bucketed by and your
custom comparer for the other columns. Never got around to try it out
o use Apache Hive and spark?
>
> Thanks.
>
> On Wed, Mar 4, 2020 at 10:40 AM lucas.g...@gmail.com
> wrote:
>
>> Or AWS glue catalog if you're in AWS
>>
>> On Wed, 4 Mar 2020 at 10:35, Magnus Nilsson wrote:
>>
>>> Google hive metastore.
>>&g
Google hive metastore.
On Wed, Mar 4, 2020 at 7:29 PM Ruijing Li wrote:
> Hi all,
>
> Has anyone explored efforts to have a centralized storage of schemas of
> different parquet files? I know there is schema management for Avro, but
> couldn’t find solutions for parquet schema management.
Well, you are posting on the Spark mailing list. Though for streaming I'd
recommend Flink over Spark any day of the week. Flink was written as a
streaming platform from the beginning quickly aligning the API with the
theoretical framework of Google's Dataflow whitepaper. It's awesome for
Hello all,
TLDR; Can the number of cores used by a task vary or is it always one core
per task? Is there a UI, metrics or logs I can check to see the number of
cores used by the task?
I have an ETL-pipeline where I do some transformations. In one of the
stages which ought to be quite CPU-heavy
Well, you should get updates every 10 seconds as long as there are events
surviving your quite aggressive watermark condition. Spark will try to drop
(not guaranteed) all events with a timestamp more than 500 milliseconds
before the current watermark timestamp. Try to increase the watermark
Row is a generic ordered collection of fields that most likely contain a
Schema of StructType. You need to keep track of the datatypes of the fields
yourself.
If you want compile time safety of datatypes (and intellisense support) you
need to use RDD:s or the Dataset[T] api. Dataset[T] might
Well, you could do a repartition on cityname/nrOfCities and use the
spark_partition_id function or the mappartitionswithindex dataframe method
to add a city Id column. Then just split the dataframe into two subsets. Be
careful of hashcollisions on the reparition Key though, or more than one
city
Since parquet don't support updates you have to backfill your dataset. If
that is your regular scenario you should partition your parquet files so
backfilling becomes easier.
As the data is structured now you have to update everything just to upsert
quite a small amount of changed data. Look at
Hello all,
How do you log what is happening inside your Spark Dataframe pipelines?
I would like to collect statistics along the way, mostly count of rows at
particular steps, to see where rows where filtered and what not. Is there
any other way to do this than calling .count on the dataframe?
import org.apache.spark.sql.expressions.Window
val partitionBy = Window.partitionBy("name", "sit").orderBy("data_date")
val newDf = df.withColumn("PreviousDate", lag("uniq_im",
1).over(partitionBy))
Cheers...
On Thu, Mar 14, 2019 at 4:55 AM anbu wrote:
> Hi,
>
> To calculate LAG functions
odify the String other than an UDF or map
> on the string?
>
>
>
> At the moment I am modifying it returning a generic type “t” so I can use
> the same UDF for many different JSONs that have the same issue.
>
>
>
> Also , is there any advantage(if possible) to extract
rt a function from 2.3 to 2.1? What other
> options do I have?
>
>
>
> Thank you.
>
>
>
>
>
> *From:* Magnus Nilsson
> *Sent:* Saturday, February 23, 2019 3:43 PM
> *Cc:* user@spark.apache.org
> *Subject:* Re: How can I parse an "unnamed" json array presen
Use spark.sql.types.ArrayType instead of a Scala Array as the root type
when you define the schema and it will work.
Regards,
Magnus
On Fri, Feb 22, 2019 at 11:15 PM Yeikel wrote:
> I have an "unnamed" json array stored in a *column*.
>
> The format is the following :
>
> column name : news
>
Hello,
I'm evaluating Structured Streaming trying to understand how resilient the
pipeline is to failures. I ran a small test streaming data from an Azure
Event Hub using Azure Databricks saving the data into a parquet file on the
Databricks filesystem dbfs:/.
I did an unclean shutdown by
I'm evaluating Structured Streaming trying to understand how resilient the
pipeline is. I ran a small test streaming data from an Azure Event Hub
using Azure Databricks saving the data into a parquet file on the
Databricks filesystem dbfs:/.
I did an unclean shutdown by cancelling the query. Then
Magnus Nilsson
9:43 AM (0 minutes ago)
to info
I had the same requirements. As far as I know the only way is to extend the
foreachwriter, cache the microbatch result and write to each output.
https://docs.databricks.com/spark/latest/structured-streaming/foreach.html
Unfortunately it seems
Hello all,
I have this peculiar problem where quote " characters are added to the
beginning and end of my string values.
I get data using Structured Streaming from an Azure Event Hub using a Scala
Notebook in Azure Databricks.
The Dataframe schema received contain a property of type Map named
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