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 <[email protected]> 发送时间: 2017年4月17日 15:59:28 收件人: 莫涛 抄送: [email protected] 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 <[email protected]> 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. > > --------------------------------------------------------------------- > To unsubscribe e-mail: [email protected] >
