Hi Michal,
Why is your solution so slow? Is it from the file IO caused by storing in a
temp file as JSON and then reading it back in and writing it as Parquet?
How are you getting "events" in the first place?
Do you have the original Kafka messages as an RDD[String]? Then how about:
1. Start with eventsAsRDD : RDD[String] (before converting to DF)
2. eventsAsRDD.map() --> use a RegEx to parse out the event_type of each
event
For example, search the string for {"event_type"="[.*]"}
3. Now, filter by each event_type to create a separate RDD for each type,
and convert those to DF. You only convert to DF for events of the same
type, so you avoid the NULLs.
Xinh
On Thu, May 5, 2016 at 2:52 AM, Michal Vince <vince.mic...@gmail.com> wrote:
> Hi Xinh
>
> For (1) the biggest problem are those null columns. e.g. DF will have
> ~1000 columns so every partition of that DF will have ~1000 columns, one of
> the partitioned columns can have 996 null columns which is big waste of
> space (in my case more than 80% in avg)
>
> for (2) I can`t really change anything as the source belongs to the 3rd
> party
>
>
> Miso
>
> On 05/04/2016 05:21 PM, Xinh Huynh wrote:
>
> Hi Michal,
>
> For (1), would it be possible to partitionBy two columns to reduce the
> size? Something like partitionBy("event_type", "date").
>
> For (2), is there a way to separate the different event types upstream,
> like on different Kafka topics, and then process them separately?
>
> Xinh
>
> On Wed, May 4, 2016 at 7:47 AM, Michal Vince <vince.mic...@gmail.com>
> wrote:
>
>> Hi guys
>>
>> I`m trying to store kafka stream with ~5k events/s as efficiently as
>> possible in parquet format to hdfs.
>>
>> I can`t make any changes to kafka (belongs to 3rd party)
>>
>>
>> Events in kafka are in json format, but the problem is there are many
>> different event types (from different subsystems with different number of
>> fields, different size etc..) so it doesn`t make any sense to store them in
>> the same file
>>
>>
>> I was trying to read data to DF and then repartition it by event_type and
>> store
>>
>> events.write.partitionBy("event_type").format("parquet").mode(org.apache.spark.sql.SaveMode.Append).save(tmpFolder)
>>
>> which is quite fast but have 2 drawbacks that I`m aware of
>>
>> 1. output folder has only one partition which can be huge
>>
>> 2. all DFs created like this share the same schema, so even dfs with few
>> fields have tons of null fields
>>
>>
>> My second try is bit naive and really really slow (you can see why in
>> code) - filter DF by event type and store them temporarily as json (to get
>> rid of null fields)
>>
>> val event_types = events.select($"event_type").distinct().collect() // get
>> event_types in this batch
>> for (row <- event_types) {
>> val currDF = events.filter($"event_type" === row.get(0))
>> val tmpPath = tmpFolder + row.get(0)
>>
>> currDF.write.format("json").mode(org.apache.spark.sql.SaveMode.Append).save(tmpPath)
>> sqlContext.read.json(tmpPath).write.format("parquet").save(basePath)
>>
>> }hdfs.delete(new Path(tmpFolder), true)
>>
>>
>> Do you have any suggestions for any better solution to this?
>>
>> thanks
>>
>>
>>
>
>
