hanks Gopal I made the following observation so far:
Using the old MR you get this message now which is fine
Hive-on-MR is deprecated in Hive 2 and may not be available in the
future versions. Consider using a different execution engine (i.e. tez,
spark) or using Hive 1.X releases.
use oraclehadoop;
--set hive.execution.engine=spark;
set hive.execution.engine=mr;
--
-- Get the total amount sold for each calendar month
--
select from_unixtime(unix_timestamp(), 'dd/MM/yyyy HH:mm:ss.ss') AS
StartTime;
CREATE TEMPORARY TABLE tmp AS
SELECT t.calendar_month_desc, c.channel_desc, SUM(s.amount_sold) AS
TotalSales
--FROM smallsales s, times t, channels c
FROM smallsales s, times t, channels c
WHERE s.time_id = t.time_id
AND s.channel_id = c.channel_id
GROUP BY t.calendar_month_desc, c.channel_desc
;
select from_unixtime(unix_timestamp(), 'dd/MM/yyyy HH:mm:ss.ss') AS
FirstQuery;
SELECT calendar_month_desc AS MONTH, channel_desc AS CHANNEL, TotalSales
from tmp
ORDER BY MONTH, CHANNEL LIMIT 5
;
select from_unixtime(unix_timestamp(), 'dd/MM/yyyy HH:mm:ss.ss') AS
SecondQuery;
SELECT channel_desc AS CHANNEL, MAX(TotalSales) AS SALES
FROM tmp
GROUP BY channel_desc
order by SALES DESC LIMIT 5
;
select from_unixtime(unix_timestamp(), 'dd/MM/yyyy HH:mm:ss.ss') AS
EndTime;
This batch returns results on MR in 2 min, 3 seconds
If I change my engine to Hive 2 on Spark 1.3.1. I get it back in 1 min,
9 sec
If I run that job on Spark 1.5.2 shell against the same tables using
Functional programming and Hive Context for tables
val HiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
println ("nStarted at"); HiveContext.sql("SELECT
FROM_unixtime(unix_timestamp(), 'dd/MM/yyyy HH:mm:ss.ss')
").collect.foreach(println)
HiveContext.sql("use oraclehadoop")
var s =
HiveContext.table("sales").select("AMOUNT_SOLD","TIME_ID","CHANNEL_ID")
val c =
HiveContext.table("channels").select("CHANNEL_ID","CHANNEL_DESC")
val t =
HiveContext.table("times").select("TIME_ID","CALENDAR_MONTH_DESC")
println ("ncreating data set at"); HiveContext.sql("SELECT
FROM_unixtime(unix_timestamp(), 'dd/MM/yyyy HH:mm:ss.ss')
").collect.foreach(println)
val rs =
s.join(t,"time_id").join(c,"channel_id").groupBy("calendar_month_desc","channel_desc").agg(sum("amount_sold").as("TotalSales"))
println ("nfirst query at"); HiveContext.sql("SELECT
FROM_unixtime(unix_timestamp(), 'dd/MM/yyyy HH:mm:ss.ss')
").collect.foreach(println)
val rs1 =
rs.orderBy("calendar_month_desc","channel_desc").take(5).foreach(println)
println ("nsecond query at"); HiveContext.sql("SELECT
FROM_unixtime(unix_timestamp(), 'dd/MM/yyyy HH:mm:ss.ss')
").collect.foreach(println)
val rs2
=rs.groupBy("channel_desc").agg(max("TotalSales").as("SALES")).orderBy("SALES").sort(desc("SALES")).take(5).foreach(println)
println ("nFinished at"); HiveContext.sql("SELECT
FROM_unixtime(unix_timestamp(), 'dd/MM/yyyy HH:mm:ss.ss')
").collect.foreach(println)
I get the job done in under 8 min. Ok this is not a benchmark for Spark
but shows that Hive 2 has improved significantly IMO. I also had Hive on
Spark 1.3.1 crashing on certain large tables(had to revert to MR) but no
issues now.
HTH
On 25/02/2016 09:13, Gopal Vijayaraghavan wrote:
>> Correct hence the question as I have done some preliminary tests on Hive 2.
>> I want to share insights with other people who have performed the same
>
> If you have feedback on Hive-2.0, I'm all ears.
>
> I'm building up 2.1 features & fixes, so now would be a good time to bring
> stuff up.
>
> Speed mostly depends on whether you're using Hive-2.0 with LLAP or not -
> if you're using the old engines, the plans still get much better (even for
> MR).
>
> Tez does get some stuff out of it, like the new shuffle join vertex
> manager (hive.optimize.dynamic.partition.hashjoin).
>
> LLAP will still win that out for <10s queries, because it takes approx ~10
> mins for all the auto-generated vectorized classes to get JIT'd into tight
> SIMD loops.
>
> For something like TPC-H Q1, you can slowly see it turning all the null
> checks into UncommonTrapBlob as the JIT slowly learns about the data &
> finds .noNulls is always true.
>
> Cheers,
> Gopal
--
Dr Mich Talebzadeh
LinkedIn
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http://talebzadehmich.wordpress.com
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