Ben,
*Also look at Phoenix (Apache project) which provides a better (one of the best) SQL/JDBC layer on top of HBase.* *http://phoenix.apache.org/ <http://phoenix.apache.org/>* I am afraid this does not work with Spark 2! 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 20:20, Thakrar, Jayesh <jthak...@conversantmedia.com> wrote: > Ben, > > > > Also look at Phoenix (Apache project) which provides a better (one of the > best) SQL/JDBC layer on top of HBase. > > http://phoenix.apache.org/ > > > > Cheers, > > Jayesh > > > > > > *From: *vincent gromakowski <vincent.gromakow...@gmail.com> > *Date: *Monday, October 17, 2016 at 1:53 PM > *To: *Benjamin Kim <bbuil...@gmail.com> > *Cc: *Michael Segel <msegel_had...@hotmail.com>, Jörn Franke < > jornfra...@gmail.com>, Mich Talebzadeh <mich.talebza...@gmail.com>, Felix > Cheung <felixcheun...@hotmail.com>, "user@spark.apache.org" < > user@spark.apache.org> > > *Subject: *Re: Spark SQL Thriftserver with HBase > > > > Instead of (or additionally to) saving results somewhere, you just start a > thriftserver that expose the Spark tables of the SQLContext (or > SparkSession now). That means you can implement any logic (and maybe use > structured streaming) to expose your data. Today using the thriftserver > means reading data from the persistent store every query, so if the data > modeling doesn't fit the query it can be quite long. What you generally do > in a common spark job is to load the data and cache spark table in a > in-memory columnar table which is quite efficient for any kind of query, > the counterpart is that the cache isn't updated you have to implement a > reload mechanism, and this solution isn't available using the thriftserver. > > What I propose is to mix the two world: periodically/delta load data in > spark table cache and expose it through the thriftserver. But you have to > implement the loading logic, it can be very simple to very complex > depending on your needs. > > > > > > 2016-10-17 19:48 GMT+02:00 Benjamin Kim <bbuil...@gmail.com>: > > Is this technique similar to what Kinesis is offering or what Structured > Streaming is going to have eventually? > > > > Just curious. > > > > Cheers, > > Ben > > > > > > On Oct 17, 2016, at 10:14 AM, vincent gromakowski < > vincent.gromakow...@gmail.com> wrote: > > > > I would suggest to code your own Spark thriftserver which seems to be very > easy. > http://stackoverflow.com/questions/27108863/accessing- > spark-sql-rdd-tables-through-the-thrift-server > > I am starting to test it. The big advantage is that you can implement any > logic because it's a spark job and then start a thrift server on temporary > table. For example you can query a micro batch rdd from a kafka stream, or > pre load some tables and implement a rolling cache to periodically update > the spark in memory tables with persistent store... > > It's not part of the public API and I don't know yet what are the issues > doing this but I think Spark community should look at this path: making the > thriftserver be instantiable in any spark job. > > > > 2016-10-17 18:17 GMT+02:00 Michael Segel <msegel_had...@hotmail.com>: > > Guys, > > Sorry for jumping in late to the game… > > > > If memory serves (which may not be a good thing…) : > > > > You can use HiveServer2 as a connection point to HBase. > > While this doesn’t perform well, its probably the cleanest solution. > > I’m not keen on Phoenix… wouldn’t recommend it…. > > > > > > The issue is that you’re trying to make HBase, a key/value object store, a > Relational Engine… its not. > > > > There are some considerations which make HBase not ideal for all use cases > and you may find better performance with Parquet files. > > > > One thing missing is the use of secondary indexing and query optimizations > that you have in RDBMSs and are lacking in HBase / MapRDB / etc … so your > performance will vary. > > > > With respect to Tableau… their entire interface in to the big data world > revolves around the JDBC/ODBC interface. So if you don’t have that piece as > part of your solution, you’re DOA w respect to Tableau. > > > > Have you considered Drill as your JDBC connection point? (YAAP: Yet > another Apache project) > > > > > > On Oct 9, 2016, at 12:23 PM, Benjamin Kim <bbuil...@gmail.com> wrote: > > > > Thanks for all the suggestions. It would seem you guys are right about the > Tableau side of things. The reports don’t need to be real-time, and they > won’t be directly feeding off of the main DMP HBase data. Instead, it’ll be > batched to Parquet or Kudu/Impala or even PostgreSQL. > > > > I originally thought that we needed two-way data retrieval from the DMP > HBase for ID generation, but after further investigation into the use-case > and architecture, the ID generation needs to happen local to the Ad Servers > where we generate a unique ID and store it in a ID linking table. Even > better, many of the 3rd party services supply this ID. So, data only needs > to flow in one direction. We will use Kafka as the bus for this. No JDBC > required. This is also goes for the REST Endpoints. 3rd party services will > hit ours to update our data with no need to read from our data. And, when > we want to update their data, we will hit theirs to update their data using > a triggered job. > > > > This al boils down to just integrating with Kafka. > > > > Once again, thanks for all the help. > > > > Cheers, > > Ben > > > > > > On Oct 9, 2016, at 3:16 AM, Jörn Franke <jornfra...@gmail.com> wrote: > > > > please keep also in mind that Tableau Server has the capabilities to store > data in-memory and refresh only when needed the in-memory data. This means > you can import it from any source and let your users work only on the > in-memory data in Tableau Server. > > > > On Sun, Oct 9, 2016 at 9:22 AM, Jörn Franke <jornfra...@gmail.com> wrote: > > Cloudera 5.8 has a very old version of Hive without Tez, but Mich provided > already a good alternative. However, you should check if it contains a > recent version of Hbase and Phoenix. That being said, I just wonder what is > the dataflow, data model and the analysis you plan to do. Maybe there are > completely different solutions possible. Especially these single inserts, > upserts etc. should be avoided as much as possible in the Big Data > (analysis) world with any technology, because they do not perform well. > > > > Hive with Llap will provide an in-memory cache for interactive analytics. > You can put full tables in-memory with Hive using Ignite HDFS in-memory > solution. All this does only make sense if you do not use MR as an engine, > the right input format (ORC, parquet) and a recent Hive version. > > > On 8 Oct 2016, at 21:55, Benjamin Kim <bbuil...@gmail.com> wrote: > > Mich, > > > > Unfortunately, we are moving away from Hive and unifying on Spark using > CDH 5.8 as our distro. And, the Tableau released a Spark ODBC/JDBC driver > too. I will either try Phoenix JDBC Server for HBase or push to move faster > to Kudu with Impala. We will use Impala as the JDBC in-between until the > Kudu team completes Spark SQL support for JDBC. > > > > Thanks for the advice. > > > > Cheers, > > Ben > > > > > > On Oct 8, 2016, at 12:35 PM, Mich Talebzadeh <mich.talebza...@gmail.com> > wrote: > > > > Sure. But essentially you are looking at batch data for analytics for your > tableau users so Hive may be a better choice with its rich SQL and > ODBC.JDBC connection to Tableau already. > > > > I would go for Hive especially the new release will have an in-memory > offering as well for frequently accessed data :) > > > > > 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 8 October 2016 at 20:15, Benjamin Kim <bbuil...@gmail.com> wrote: > > Mich, > > > > First and foremost, we have visualization servers that run Tableau for > external user reports. Second, we have servers that are ad servers and REST > endpoints for cookie sync and segmentation data exchange. These will use > JDBC directly within the same data-center. When not colocated in the same > data-center, they will connected to a located database server using JDBC. > Either way, by using JDBC everywhere, it simplifies and unifies the code on > the JDBC industry standard. > > > > Does this make sense? > > > > Thanks, > > Ben > > > > > > On Oct 8, 2016, at 11:47 AM, Mich Talebzadeh <mich.talebza...@gmail.com> > wrote: > > > > Like any other design what is your presentation layer and end users? > > > > Are they SQL centric users from Tableau background or they may use spark > functional programming. > > > > It is best to describe the use case. > > > > HTH > > > 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 8 October 2016 at 19:40, Felix Cheung <felixcheun...@hotmail.com> > wrote: > > I wouldn't be too surprised Spark SQL - JDBC data source - Phoenix JDBC > server - HBASE would work better. > > > > Without naming specifics, there are at least 4 or 5 different > implementations of HBASE sources, each at varying level of development and > different requirements (HBASE release version, Kerberos support etc) > > > > > > _____________________________ > From: Benjamin Kim <bbuil...@gmail.com> > Sent: Saturday, October 8, 2016 11:26 AM > Subject: Re: Spark SQL Thriftserver with HBase > To: Mich Talebzadeh <mich.talebza...@gmail.com> > Cc: <user@spark.apache.org>, Felix Cheung <felixcheun...@hotmail.com> > > > > > Mich, > > > > Are you talking about the Phoenix JDBC Server? If so, I forgot about that > alternative. > > > > Thanks, > > Ben > > > > > > On Oct 8, 2016, at 11:21 AM, Mich Talebzadeh <mich.talebza...@gmail.com> > wrote: > > > > I don't think it will work > > > > you can use phoenix on top of hbase > > > > hbase(main):336:0> scan 'tsco', 'LIMIT' => 1 > ROW COLUMN+CELL > TSCO-1-Apr-08 > column=stock_daily:Date, timestamp=1475866783376, value=1-Apr-08 > TSCO-1-Apr-08 > column=stock_daily:close, timestamp=1475866783376, value=405.25 > TSCO-1-Apr-08 > column=stock_daily:high, timestamp=1475866783376, value=406.75 > TSCO-1-Apr-08 > column=stock_daily:low, timestamp=1475866783376, value=379.25 > TSCO-1-Apr-08 > column=stock_daily:open, timestamp=1475866783376, value=380.00 > TSCO-1-Apr-08 > column=stock_daily:stock, timestamp=1475866783376, value=TESCO PLC > TSCO-1-Apr-08 > column=stock_daily:ticker, timestamp=1475866783376, value=TSCO > TSCO-1-Apr-08 > column=stock_daily:volume, timestamp=1475866783376, value=49664486 > > > > And the same on Phoenix on top of Hvbase table > > > > 0: jdbc:phoenix:thin:url=http://rhes564:8765> select > substr(to_char(to_date("Date",'dd-MMM-yy')),1,10) AS TradeDate, "close" > AS "Day's close", "high" AS "Day's High", "low" AS "Day's Low", "open" AS > "Day's Open", "ticker", "volume", (to_number("low")+to_number("high"))/2 > AS "AverageDailyPrice" from "tsco" where to_number("volume") > 0 and "high" > != '-' and to_date("Date",'dd-MMM-yy') > to_date('2015-10-06','yyyy-MM-dd') > order by to_date("Date",'dd-MMM-yy') limit 1; > +-------------+--------------+-------------+------------+--- > ----------+---------+-----------+--------------------+ > | TRADEDATE | Day's close | Day's High | Day's Low | Day's Open | > ticker | volume | AverageDailyPrice | > +-------------+--------------+-------------+------------+--- > ----------+---------+-----------+--------------------+ > | 2015-10-07 | 197.00 | 198.05 | 184.84 | 192.20 | > TSCO | 30046994 | 191.445 | > > > > HTH > > > > > > > > > > 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 destructionof 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 > suchloss, damage or destruction. > > > > > > On 8 October 2016 at 19:05, Felix Cheung <felixcheun...@hotmail.com> > wrote: > > Great, then I think those packages as Spark data source should allow you > to do exactly that (replace org.apache.spark.sql.jdbc with HBASE one) > > > > I do think it will be great to get more examples around this though. Would > be great if you could share your experience with this! > > > > > > _____________________________ > From: Benjamin Kim <bbuil...@gmail.com> > Sent: Saturday, October 8, 2016 11:00 AM > Subject: Re: Spark SQL Thriftserver with HBase > To: Felix Cheung <felixcheun...@hotmail.com> > Cc: <user@spark.apache.org> > > > Felix, > > > > My goal is to use Spark SQL JDBC Thriftserver to access HBase tables using > just SQL. I have been able to CREATE tables using this statement below in > the past: > > > > CREATE TABLE <table-name> > > USING org.apache.spark.sql.jdbc > > OPTIONS ( > > url "jdbc:postgresql://<hostname>:<port>/dm?user=<username>& > password=<password>", > > dbtable "dim.dimension_acamp" > > ); > > > > After doing this, I can access the PostgreSQL table using Spark SQL JDBC > Thriftserver using SQL statements (SELECT, UPDATE, INSERT, etc.). I want to > do the same with HBase tables. We tried this using Hive and HiveServer2, > but the response times are just too long. > > > > Thanks, > > Ben > > > > > > On Oct 8, 2016, at 10:53 AM, Felix Cheung <felixcheun...@hotmail.com> > wrote: > > > > Ben, > > > > I'm not sure I'm following completely. > > > > Is your goal to use Spark to create or access tables in HBASE? If so the > link below and several packages out there support that by having a HBASE > data source for Spark. There are some examples on how the Spark code look > like in that link as well. On that note, you should also be able to use the > HBASE data source from pure SQL (Spark SQL) query as well, which should > work in the case with the Spark SQL JDBC Thrift Server (with USING, > http://spark.apache.org/docs/latest/sql-programming-guide.html#tab_sql_10 > ). > > > > _____________________________ > From: Benjamin Kim <bbuil...@gmail.com> > Sent: Saturday, October 8, 2016 10:40 AM > Subject: Re: Spark SQL Thriftserver with HBase > To: Felix Cheung <felixcheun...@hotmail.com> > Cc: <user@spark.apache.org> > > > Felix, > > > > The only alternative way is to create a stored procedure (udf) in database > terms that would run Spark scala code underneath. In this way, I can use > Spark SQL JDBC Thriftserver to execute it using SQL code passing the key, > values I want to UPSERT. I wonder if this is possible since I cannot CREATE > a wrapper table on top of a HBase table in Spark SQL? > > > > What do you think? Is this the right approach? > > > > Thanks, > > Ben > > > > On Oct 8, 2016, at 10:33 AM, Felix Cheung <felixcheun...@hotmail.com> > wrote: > > > > HBase has released support for Spark > > hbase.apache.org/book.html#spark > > > > And if you search you should find several alternative approaches. > > > > > > On Fri, Oct 7, 2016 at 7:56 AM -0700, "Benjamin Kim" <bbuil...@gmail.com> > wrote: > > Does anyone know if Spark can work with HBase tables using Spark SQL? I > know in Hive we are able to create tables on top of an underlying HBase > table that can be accessed using MapReduce jobs. Can the same be done using > HiveContext or SQLContext? We are trying to setup a way to GET and POST > data to and from the HBase table using the Spark SQL JDBC thriftserver from > our RESTful API endpoints and/or HTTP web farms. If we can get this to > work, then we can load balance the thriftservers. In addition, this will > benefit us in giving us a way to abstract the data storage layer away from > the presentation layer code. There is a chance that we will swap out the > data storage technology in the future. We are currently experimenting with > Kudu. > > Thanks, > Ben > --------------------------------------------------------------------- > To unsubscribe e-mail: user-unsubscr...@spark.apache.org > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
