Re: Spark SQL Thriftserver with HBase
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<mailto: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<mailto: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<mailto: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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw http://talebzadehmich.wordpress.com<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<mailto: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<mailto: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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw http://talebzadehmich.wordpress.com<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<mailto: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<mailto: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<mailto:mich.talebza...@gmail.com>> Cc: <user@spark.apache.org<mailto:user@spark.apache.org>>, Felix Cheung <felixcheun...@hotmail.com<mailto: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
Re: Spark SQL Thriftserver with HBase
; >> 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >>> >>> >>> 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>> <https://www.linkedin.com/profi
Re: Spark SQL Thriftserver with HBase
;> 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>>>>>>> >>>>>>>>> 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 withi
Re: Spark SQL Thriftserver with HBase
ntil 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<mailto: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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw http://talebzadehmich.wordpress.com<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<mailto: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<mailto: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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw http://talebzadehmich.wordpress.com<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<mailto: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<mailto: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<mailto:mich.talebza...@gmail.com>> Cc: <user@spark.apache.org<mailto:user@spark.apache.org>>, Felix Cheung <felixcheun...@hotmail.com<mailto: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<mailto: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<http://rhes564:8765/>>
Re: Spark SQL Thriftserver with HBase
Skip Phoenix On Oct 17, 2016, at 2:20 PM, Thakrar, Jayesh <jthak...@conversantmedia.com<mailto: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<mailto:vincent.gromakow...@gmail.com>> Date: Monday, October 17, 2016 at 1:53 PM To: Benjamin Kim <bbuil...@gmail.com<mailto:bbuil...@gmail.com>> Cc: Michael Segel <msegel_had...@hotmail.com<mailto:msegel_had...@hotmail.com>>, Jörn Franke <jornfra...@gmail.com<mailto:jornfra...@gmail.com>>, Mich Talebzadeh <mich.talebza...@gmail.com<mailto:mich.talebza...@gmail.com>>, Felix Cheung <felixcheun...@hotmail.com<mailto:felixcheun...@hotmail.com>>, "user@spark.apache.org<mailto:user@spark.apache.org>" <user@spark.apache.org<mailto: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<mailto: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<mailto: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<mailto: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<mailto: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 t
Re: Spark SQL Thriftserver with HBase
gBxianrbJd6zP6AcPCCdOABUrV8Pw
http://talebzadehmich.wordpress.com<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<mailto: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<mailto: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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
http://talebzadehmich.wordpress.com<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<mailto: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<mailto: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<mailto:mich.talebza...@gmail.com>>
Cc: <user@spark.apache.org<mailto:user@spark.apache.org>>, Felix Cheung
<felixcheun...@hotmail.com<mailto: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<mailto: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<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','-MM-dd') order by
to_date("Date",'dd-MMM-yy') lim
Re: Spark SQL Thriftserver with HBase
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<mailto: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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw http://talebzadehmich.wordpress.com<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<mailto: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<mailto: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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw http://talebzadehmich.wordpress.com<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<mailto: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<mailto: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<mailto:mich.talebza...@gmail.com>> Cc: <user@spark.apache.org<mailto:user@spark.apache.org>>, Felix Cheung <felixcheun...@hotmail.com<mailto: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<mailto: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=496644
Re: Spark SQL Thriftserver with HBase
s 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>>>>> >>>>>>> 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>>>>>>> >>>>>>>>> http://talebzadehmich.wordpress.com >>>>>>>>> >>>>>>>>> Disclaimer: Use i
Re: Spark SQL Thriftserver with HBase
> damages arising from such loss, damage or destruction. >>>>>> >>>>>> >>>>>> On 8 October 2016 at 20:15, Benjamin Kim <bbuil...@gmail.com >>>>>> <mailto: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 >>>>>>> <mailto: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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>>>>> >>>>>>> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw> >>>>>>> >>>>>>> http://talebzadehmich.wordpress.com >>>>>>> <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 >>>>>>> <mailto: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 <mailto: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 >>>>>>> <mailto:mich.talebza...@gmail.com>> >>>>>>> Cc: <user@spark.apache.org <mailto:user@spark.apache.org>>, Felix >>>>>>> Cheung <felixcheun...@hotmail.com <mailto: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 &g
Re: Spark SQL Thriftserver with HBase
ring as well for frequently accessed data :) >> >> >> Dr Mich Talebzadeh >> >> >> LinkedIn * >> https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >> >> >> 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >>> >>> >>> 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=3
Re: Spark SQL Thriftserver with HBase
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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* 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 bi
Re: Spark SQL Thriftserver with HBase
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<mailto: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<mailto: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<mailto: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<mailto: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 th
Re: Spark SQL Thriftserver with HBase
ying 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >>>> >>>> >>>> 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>>> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >>>>> >>>>> >>>>> 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
Re: Spark SQL Thriftserver with HBase
>> 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >>> >>> >>> 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >>>> >>&g
Re: Spark SQL Thriftserver with HBase
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 >>> <mailto: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 >>>>> <mailto: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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>>> >>>>> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw> >>>>> >>>>> http://talebzadehmich.wordpress.com <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 >>>>> <mailto: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 >>>>>> <mailto: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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>>>>> >>>>>> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw> >>>>>> >>>>>> http://talebzadehmich.wordpress.com >>>
Re: Spark SQL Thriftserver with HBase
tween 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >> >> >> 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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw >>> <https://www.linkedin.com/profile/view?id=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* >>> >>> >>> 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 >>&g
Re: Spark SQL Thriftserver with HBase
lt;bbuil...@gmail.com<mailto: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<mailto: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=AAEWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
http://talebzadehmich.wordpress.com<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<mailto: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<mailto: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<mailto:mich.talebza...@gmail.com>>
Cc: <user@spark.apache.org<mailto:user@spark.apache.org>>, Felix Cheung
<felixcheun...@hotmail.com<mailto: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<mailto: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<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','-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 Mic
