One possihility is using hive with bucketed on id column?
Another option: build the index in hbase ie store id and path of hdfs in
hbase. This was your scans will be fast and once you have the hdfs path
pointers you can read the actual data from hdfs.
On Mon, 17 Apr 2017 at 6:52 pm, 莫涛 <mo...@sensetime.com> wrote:
> Hi Jörn,
>
>
> I do think a 5 MB column is odd but I don't have any other idea before
> asking this question. The binary data is a short video and the maximum
> size is no more than 50 MB.
>
>
> Hadoop archive sounds very interesting and I'll try it first to check
> whether filtering is fast on it.
>
>
> To my best knowledge, HBase works best for record around hundreds of KB
> and it requires extra work of the cluster administrator. So this would be
> the last option.
>
>
> Thanks!
>
>
> Mo Tao
> ------------------------------
> *发件人:* Jörn Franke <jornfra...@gmail.com>
> *发送时间:* 2017年4月17日 15:59:28
> *收件人:* 莫涛
> *抄送:* user@spark.apache.org
>
> *主题:* Re: How to store 10M records in HDFS to speed up further filtering?
> You need to sort the data by id otherwise q situation can occur where the
> index does not work. Aside from this, it sounds odd to put a 5 MB column
> using those formats. This will be also not so efficient.
> What is in the 5 MB binary data?
> You could use HAR or maybe Hbase to store this kind of data (if it does
> not get much larger than 5 MB).
>
> > On 17. Apr 2017, at 08:23, MoTao <mo...@sensetime.com> wrote:
> >
> > Hi all,
> >
> > I have 10M (ID, BINARY) record, and the size of each BINARY is 5MB on
> > average.
> > In my daily application, I need to filter out 10K BINARY according to an
> ID
> > list.
> > How should I store the whole data to make the filtering faster?
> >
> > I'm using DataFrame in Spark 2.0.0 and I've tried row-based format (avro)
> > and column-based format (orc).
> > However, both of them require to scan almost ALL records, making the
> > filtering stage very very slow.
> > The code block for filtering looks like:
> >
> > val IDSet: Set[String] = ...
> > val checkID = udf { ID: String => IDSet(ID) }
> > spark.read.orc("/path/to/whole/data")
> > .filter(checkID($"ID"))
> > .select($"ID", $"BINARY")
> > .write...
> >
> > Thanks for any advice!
> >
> >
> >
> >
> > --
> > View this message in context:
> http://apache-spark-user-list.1001560.n3.nabble.com/How-to-store-10M-records-in-HDFS-to-speed-up-further-filtering-tp28605.html
> > Sent from the Apache Spark User List mailing list archive at Nabble.com.
> >
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> >
>
--
Best Regards,
Ayan Guha
