Thanks Mike, My test csv data comes as
UUID, ticker, timecreated, price a2c844ed-137f-4820-aa6e-c49739e46fa6, S01, 2016-10-17T22:02:09, 53.36665625650533484995 a912b65e-b6bc-41d4-9e10-d6a44ea1a2b0, S02, 2016-10-17T22:02:09, 86.31917515824627016510 5f4e3a9d-05cc-41a2-98b3-40810685641e, S03, 2016-10-17T22:02:09, 95.48298277703729129559 And this is my Hbase table with one column family create 'marketDataHbase', 'price_info' It is populated every 15 minutes from test.csv files delivered via Kafka and Flume to HDFS 1. Create a fat csv file based on all small csv files for today --> prices/2016-10-17 2. Populate data into Hbase table using org.apache.hadoop.hbase.mapreduce.ImportTsv 3. This is pretty quick using MapReduce That importTsv only appends new rows to Hbase table as the choice of UUID as rowKey avoids any issues. So I only have 15 minutes lag in my batch Hbase table. I have both Hive ORC tables and Phoenix views on top of this Hbase tables. 1. Phoenix offers the fastest response if used on top of Hbase. unfortunately, Spark 2 with Phoenix is broken 2. Spark on Hive on Hbase looks OK. This works fine with Spark 2 3. Spark on Hbase tables directly using key, value DFs for each column. Not as fast as 2 but works. I don't think a DF is a good choice for a key, value pair? Now if I use Zeppelin to read from Hbase 1. I can use Phoenix JDBC. That looks very fast 2. I can use Spark csv directly on HDFS csv files. 3. I can use Spark on Hive tables If I use Tableau on Hbase data then, only sql like code is useful. Phoenix or Hive I don't want to change the design now. But admittedly Hive is the best SQL on top of Hbase. Next release of Hive is going to have in-memory database (LLAP) so we can cache Hive tables in memory. That will be faster. Many people underestimate Hive but I still believe it has a lot to offer besides serious ANSI compliant SQL. Regards Mich Dr Mich Talebzadeh LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* http://talebzadehmich.wordpress.com *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On 17 October 2016 at 21:54, Michael Segel <msegel_had...@hotmail.com> wrote: > Mitch, > > Short answer… no, it doesn’t scale. > > Longer answer… > > You are using an UUID as the row key? Why? (My guess is that you want to > avoid hot spotting) > > So you’re going to have to pull in all of the data… meaning a full table > scan… and then perform a sort order transformation, dropping the UUID in > the process. > > You would be better off not using HBase and storing the data in Parquet > files in a directory partitioned on date. Or rather the rowkey would be > the max_ts - TS so that your data is in LIFO. > Note: I’ve used the term epoch to describe the max value of a long (8 > bytes of ‘FF’ ) for the max_ts. This isn’t a good use of the term epoch, > but if anyone has a better term, please let me know. > > > > Having said that… if you want to use HBase, you could do the same thing. > If you want to avoid hot spotting, you could load the day’s transactions > using a bulk loader so that you don’t have to worry about splits. > > But that’s just my $0.02 cents worth. > > HTH > > -Mike > > PS. If you wanted to capture the transactions… you could do the following > schemea: > > 1) Rowkey = max_ts - TS > 2) Rows contain the following: > CUSIP (Transaction ID) > Party 1 (Seller) > Party 2 (Buyer) > Symbol > Qty > Price > > This is a trade ticket. > > > > On Oct 16, 2016, at 1:37 PM, Mich Talebzadeh <mich.talebza...@gmail.com> > wrote: > > Hi, > > I have trade data stored in Hbase table. Data arrives in csv format to > HDFS and then loaded into Hbase via periodic load with > org.apache.hadoop.hbase.mapreduce.ImportTsv. > > The Hbase table has one Column family "trade_info" and three columns: > ticker, timecreated, price. > > The RowKey is UUID. So each row has UUID, ticker, timecreated and price in > the csv file > > Each row in Hbase is a key, value map. In my case, I have one Column > Family and three columns. Without going into semantics I see Hbase as a > column oriented database where column data stay together. > > So I thought of this way of accessing the data. > > I define an RDD for each column in the column family as below. In this > case column trade_info:ticker > > //create rdd > val hBaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat], > classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable], > classOf[org.apache.hadoop.hbase.client.Result]) > val rdd1 = hBaseRDD.map(tuple => tuple._2).map(result => (result.getRow, > result.getColumn("price_info".getBytes(), "ticker".getBytes()))).map(row > => { > ( > row._1.map(_.toChar).mkString, > row._2.asScala.reduceLeft { > (a, b) => if (a.getTimestamp > b.getTimestamp) a else b > }.getValue.map(_.toChar).mkString > ) > }) > case class columns (key: String, ticker: String) > val dfticker = rdd1.toDF.map(p => columns(p(0).toString,p(1).toString)) > > Note that the end result is a DataFrame with the RowKey -> key and column > -> ticker > > I use the same approach to create two other DataFrames, namely dftimecreated > and dfprice for the two other columns. > > Note that if I don't need a column, then I do not create a DF for it. So a > DF with each column I use. I am not sure how this compares if I read the > full row through other methods if any. > > Anyway all I need to do after creating a DataFrame for each column is to > join themthrough RowKey to slice and dice data. Like below. > > Get me the latest prices ordered by timecreated and ticker (ticker is > stock) > > val rs = > dfticker.join(dftimecreated,"key").join(dfprice,"key").orderBy('timecreated > desc, 'price desc).select('timecreated, 'ticker, > 'price.cast("Float").as("Latest > price")) > rs.show(10) > > +-------------------+------+------------+ > | timecreated|ticker|Latest price| > +-------------------+------+------------+ > |2016-10-16T18:44:57| S16| 97.631966| > |2016-10-16T18:44:57| S13| 92.11406| > |2016-10-16T18:44:57| S19| 85.93021| > |2016-10-16T18:44:57| S09| 85.714645| > |2016-10-16T18:44:57| S15| 82.38932| > |2016-10-16T18:44:57| S17| 80.77747| > |2016-10-16T18:44:57| S06| 79.81854| > |2016-10-16T18:44:57| S18| 74.10128| > |2016-10-16T18:44:57| S07| 66.13622| > |2016-10-16T18:44:57| S20| 60.35727| > +-------------------+------+------------+ > only showing top 10 rows > > Is this a workable solution? > > Thanks > > > > Dr Mich Talebzadeh > > > LinkedIn * > https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw > <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* > > > http://talebzadehmich.wordpress.com > > *Disclaimer:* Use it at your own risk. Any and all responsibility for any > loss, damage or destruction of data or any other property which may arise > from relying on this email's technical content is explicitly disclaimed. > The author will in no case be liable for any monetary damages arising from > such loss, damage or destruction. > > > > >
