I tried dataframe writer with coalesce or repartition api, but it can not
meet my requirements, I still can get far more files than bucket number,
and spark jobs is very slow after I add coalesce or repartition.
I've get back to Hive, use Hive to do data conversion.
Thanks.
On Sat, Sep 17, 2016 at 11:12 PM, Mich Talebzadeh <mich.talebza...@gmail.com
> wrote:
> Ok
>
> You have an external table in Hive on S3 with partition and bucket. say
>
> ......
> PARTITIONED BY (year int, month string)
> CLUSTERED BY (prod_id) INTO 256 BUCKETS
> STORED AS ORC.....
>
> with have within each partition buckets on prod_id equally spread to 256
> hash partitions/bucket. bucket is the hash partitioning within a Hive table
> partition.
>
> Now my question is how do you force data to go for a given partition p
> into bucket n. Since you have already specified say 256 buckets then
> whatever prod_id is, it still has to go to one of 256 buckets.
>
> Within Spark , the number of files is actually the number of underlying
> RDD partitions. You can find this out by invoking toJavaRDD.partitions.size()
> and force it to accept a certain number of partitions by using coalesce(n)
> or something like that. However, I am not sure the output will be what you
> expect to be.
>
> Worth trying to sort it out the way you want with partition 8
>
> val HiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
> val s = spark.read.parquet("oraclehadoop.sales2")
> s.coalesce(8).registerTempTable("tmp")
> HiveContext.sql("SELECT * FROM tmp SORT BY prod_id").write.mode("
> overwrite").parquet("test.sales6")
>
>
> It may work.
>
> HTH
>
>
>
> Dr Mich Talebzadeh
>
>
>
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> On 17 September 2016 at 15:00, Qiang Li <q...@appannie.com> wrote:
>
>> I want to run job to load existing data from one S3 bucket, process it,
>> then store to another bucket with Partition, and Bucket (data format
>> conversion from tsv to parquet with gzip). So source data and results both
>> are in S3, different are the tools which I used to process data.
>>
>> First I process data with Hive, create external tables with s3 location
>> with partition and bucket number, jobs will generate files under each
>> partition directory, and it was equal bucket number.
>> then everything is ok, I also can use hive/presto/spark to run other jobs
>> on results data in S3.
>>
>> But if I run spark job with partition and bucket, sort feature, spark job
>> will generate far more files than bucket number under each partition
>> directory, so presto or hive can not recongnize the bucket because wrong
>> files number is not equal bucket number in spark job.
>>
>> for example:
>> ...
>> val options = Map("path" -> "result_bucket_path", "compression" -> "gzip")
>> res.write.mode("append").format("parquet").partitionBy("year", "month",
>> "day").bucketBy(8, "xxx_id").sortBy("xxx_id").opt
>> ions(options).saveAsTable("result_bucket_name")
>> ...
>>
>> The results bucket files under each partition is far more than 8.
>>
>>
>> On Sat, Sep 17, 2016 at 9:27 PM, Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> It is difficult to guess what is happening with your data.
>>>
>>> First when you say you use Spark to generate test data are these
>>> selected randomly and then stored in Hive/etc table?
>>>
>>> HTH
>>>
>>> Dr Mich Talebzadeh
>>>
>>>
>>>
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>>> On 17 September 2016 at 13:59, Qiang Li <q...@appannie.com> wrote:
>>>
>>>> Hi,
>>>>
>>>> I use spark to generate data , then we use hive/pig/presto/spark to
>>>> analyze data, but I found even I add used bucketBy and sortBy with bucket
>>>> number in Spark, the results files was generate by Spark is always far more
>>>> than bucket number under each partition, then Presto can not recognize the
>>>> bucket, how can I control that in Spark ?
>>>>
>>>> Unfortunately, I did not find any way to do that.
>>>>
>>>> Thank you.
>>>>
>>>> --
>>>> Adam - App Annie Ops
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>>>
>>>
>>
>>
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