About nramanaiah

nramanaiah · ‎06-24-2019

This WARN logs should not cause any issue, 1) however if you want to remove this configs, you can use below syntax to delete the config on specific component config_type(Configs can be searched in ambari -> hive configs filter box to know which file to be updated) /var/lib/ambari-server/resources/scripts/configs.py -u <<username>> -p <<password>> -n <<clustername>> -l <<ambari-server-host>> -t <<ambari-server-port>> -a <<action>> -c <<config_type>> -k <<config-key>> eg., /var/lib/ambari-server/resources/scripts/configs.py -u admin -p <<dummy>> -n cluster1 -l ambari-server-host -t 8080 -a delete -c hive-site -k hive.mapred.strict /var/lib/ambari-server/resources/scripts/configs.py -u admin -p <<dummy>> -n cluster1 -l ambari-server-host -t 8080 -a delete -c hive-site -k hive.mapred.supports.subdirectories This is the reference for configs.py https://cwiki.apache.org/confluence/display/AMBARI/Modify+configurations#Modifyconfigurations-Editconfigurationusingconfigs.py 2) To remove log4j warning, goto ambari -> hive configs -> advance hive-log4j, comment below line log4j.appender.DRFA.MaxFileSize After the above 2 changes, restart hive services, all those 3 warns should go away. If this article helps to resolve the issue, accept the answer, it might also help others members in the community.

nramanaiah · ‎04-22-2019

You can try below query to replace empty results instead of actual value select regexp_replace(salary, ".*", "") from employee; If the intension is to mask a column, you can use ranger masking policy for the table column or use mask udf's eg., select mask(salary) from employee;

nramanaiah · ‎04-22-2019

with t as (select *,row_number() over(partition by statename order by value desc ) as num from air_quality where measureid = 87 and reportyear >= 2008 and reportyear <= 2013) select statename, measureid, reportyear, value from t where num=1 order by value desc limit 5

nramanaiah · ‎01-14-2019

If the input is text data, its better to write a java utility which reads line by line, applies all the rules & re-writes it as four column as per conversion required. Then load that new file into hive to query it. It will be cumbursome to do it in sql, as it needs another case statement inside case statement to validate the length of the split.

nramanaiah · ‎01-11-2019

You can use below sql to convert the content as required 0: jdbc:hive2://localhost> select * from temptable; +---------------------+--+ | temptable.fullname | +---------------------+--+ | abc xxx! def ghi | | abc systems! | +---------------------+--+ 0: jdbc:hive2://localhost> select a[0] as LAST, substr(a[1], 0, length(a[1])-1) as TITLE, a[2] as FIRST, a[3] as MID from ( 0: jdbc:hive2://localhost> select case 0: jdbc:hive2://localhost> when instr(fullname, "!" )=length(fullname) then array('', '!', substr(fullname, 0, length(fullname)-1), '') 0: jdbc:hive2://localhost> else split(fullname, " ") end as a from temptable) t; +-------+--------+--------------+------+--+ | last | title | first | mid | +-------+--------+--------------+------+--+ | abc | xxx | def | ghi | | | | abc systems | | +-------+--------+--------------+------+--+ If my answer helped to resolve the issue, please accept. It might help others in the community.

nramanaiah · ‎10-15-2018

Bucketed tables allows much more efficient sampling than non-bucketed tables. With sampling, we can try out queries on a section of data for testing and debugging purpose when the original data sets are very huge. False positive is possible on clustered by columns, whereas applying filters on partition column is better in pruning the exact files. Bucketing tables also helps in map-join, assuming the data is equally distributed. As it eliminates the cost of partitioning the data while joining. Bucketing is useful with high cardinality columns, as it reduces number of small files & clusters them within specified buckets. Earlier to HIVE-11525, bucketing wasn't able to filter files if multiple columns are clustered by.

nramanaiah · ‎10-13-2018

In count(*) query, final aggregation vertex should always be single task which fetches count of all the mappers & sumup all of the them. If my response helped your query, accept the answer. It might help others in the community.

nramanaiah · ‎10-13-2018

Assume below sample data name,age,dept 'naresh',25,'IT' 'sami',24,'ECE' Creating a table with partitioned by(name) clustered by(age,dept) into 1 bucket, 2 partition folders inside table path <table_path>/name=naresh/000000_0 <table_path>/name=sami/000000_0 Whereas incase of clustered by(name,age,dept) into 1 bucket, hashcode(name,age,dept) % Number of buckets Clustered by scatters the data within buckets based on hashcode, whereas incase of partition by, u will have explicit folder per unique value of a column which gives better performance if filters applied on partition column. If my response helped your query, accept the answer. It might help others in the community.

nramanaiah · ‎10-13-2018

Yes, indexes automatically help in filtering data when your select query has filters on indexed column. You dont need to explicitily select from index.

nramanaiah · ‎10-11-2018

HIVE-20593 is fixed in Hive Opensource community, I am suspecting implementing this code fix in your current cluster will help to resolve your issue. You can contact hortonworks support to validate my suspect & get hotfix.

nramanaiah · ‎10-10-2018

Suspecting HIVE-20593 should help to fix this issue.

nramanaiah · ‎10-10-2018

To see all values of a session in 1)HS2 beeline --outputformat=tsv2 -u '<HS2_JDBC_URL> -e 'set -v' > session.txt 2) Cli hive --outputformat=tsv2 -e 'set -v' > session.txt If specific key to be searched, then after launching beeline or cli, use the specific keys as below beeline> set hive.exec.dynamic.partition.mode; If my suggestion helped to resolve your query, accept the answer. It might help others in the community.

nramanaiah · ‎10-10-2018

Glad to hear that the issue is resolved. You can either create local folder & give permissions or Hive will create hive.exec.local.scratchdir & provide permissions as configured in hive.scratch.dir.permission Provided if the user is having privileges on parent directory. If my answer helped to resolve the issue, accept the answer. It might help others in the community.

nramanaiah · ‎10-09-2018

Assuming you want to connect to HS2, apart from beeline, you can connect using JDBC/ODBC as explained in wiki.

nramanaiah · ‎10-05-2018

Please try the below query, it should return required results select text from t1 join match where instr(text, hint) !=0; If my suggestion helped to solve the problem, accept the answer. It might help others in the community.

nramanaiah · ‎10-05-2018

This issue is happening because user infa does not have privilege to create local directory in /tmp/ You can launch hive with custom local directory which might have privileges for infa user. hive --hiveconf hive.exec.local.scratchdir=<local machine path>

nramanaiah · ‎10-04-2018

When applying order by twice, hive assumes column selection order is same on both inner & outer query which does order by on wrong column assuming wrong column datatype. You can rewrite your query having outer & inner query projection as same. SELECT t_6.color color, t_6.noq_empty noq_empty FROM (SELECT t_5.color_gbakc2 color, t_5.noq_empty_gbakc3 noq_empty FROM (SELECT testtable.color_gbakc2 color_gbakc2, testtable.noq_empty_gbakc3 noq_empty_gbakc3, testtable.color_gbakc1 color_gbakc1 FROM testtable testtable WHERE testtable.color_gbakc2 IN ('Red', 'Blue', 'Green') ) t_5 WHERE t_5.color_gbakc2 IN ('Red', 'Blue') ORDER BY noq_empty ASC ) t_6 ORDER BY color Fortunately HIVE-6348 is a plan optimizer & its eliminating this issue also. If my answer helped you, accept the answer. It will help others in the community.

nramanaiah · ‎10-04-2018

Statistics estimation is approximated based on available stats. In your case, i see basic stats are collected, but column stats are not collected. To estimate the number of distinct values for a column, can you run analyze table temp.test_distinct compute statistics for columns; and check explain plan.

nramanaiah · ‎10-04-2018

HIVE-6348 should resolve this issue. I don't see any workaround for this issue. This fix is available in HDP-3

nramanaiah · ‎10-03-2018

Probably you might be hitting TEZ-2741. can you try to run the query by setting below config hive.compute.splits.in.am=false

nramanaiah · ‎09-28-2018

If First day of week should start from Monday, change the subtraction/addition date to 1900-01-08 --First day of the week as Monday select date_sub('2018-09-12',pmod(datediff('2018-09-12','1900-01-08'),7)); +-------------+--+ | _c0 | +-------------+--+ | 2018-09-10 | +-------------+--+ --Last day of the week as Sunday select date_add('2018-09-12',6 - pmod(datediff('2018-09-12','1900-01-08'),7)); +-------------+--+ | _c0 | +-------------+--+ | 2018-09-16 | +-------------+--+

nramanaiah · ‎09-27-2018

Create table using appropriate delimiters as shown in below create statement # create table complextype(field1 string, field2 string, field3 struct<field31:string,field32:string>, field4 string) row format delimited fields terminated by ',' collection items terminated by '$' stored as textfile; Create CSV files using appropriate delimiters. In below sample csv data, fields are terminated by comma(,) & complex data within the field is terminated by dollar($) # hadoop fs -cat /tmp/complex.csv value1,value2,value31$value32,value4 Load & query data # load data inpath '/tmp/complex.csv' into table complextype; # select * from complextype; +---------------------+---------------------+--------------------------------------------+---------------------+--+ | complextype.field1 | complextype.field2 | complextype.field3 | complextype.field4 | +---------------------+---------------------+--------------------------------------------+---------------------+--+ | value1 | value2 | {"field31":"value31","field32":"value32"} | value4 | +---------------------+---------------------+--------------------------------------------+---------------------+--+ If my answer helps to solve your problem, accept the answer. It might help others in the community.

nramanaiah · ‎09-25-2018

Using SQL -- First Day of the week select date_sub('2018-09-25',pmod(datediff('2018-09-25','1900-01-07'),7)); +-------------+--+ | _c0 | +-------------+--+ | 2018-09-23 | +-------------+--+ -- Last Day of the week select date_add('2018-09-25',6 - pmod(datediff('2018-09-25','1900-01-07'),7)); +-------------+--+ | _c0 | +-------------+--+ | 2018-09-29 | +-------------+--+ If my answer helped to solve your problem, accept the answer. It might help others in the community.

nramanaiah · ‎09-25-2018

You can write a custom UDF in Hive to pick any day of the week. You can refer lastDay UDF code as an example // Code in Custom UDF FirstDayOfWeek SimpleDateFormat formatter = new SimpleDateFormat("yyyy-MM-dd"); Calendar calendar = Calendar.getInstance(); calendar.setTime(formatter.parse("2018-09-16")); // Actual Date string column here calendar.set(Calendar.DAY_OF_WEEK, Calendar.SUNDAY); System.out.println(calendar.getTime());

nramanaiah · ‎09-22-2018

If query progress along with number of mapper & reducer task are not shown in console, please check below properties are set to true. hive.server2.in.place.progress hive.tez.exec.inplace.progress

nramanaiah · ‎09-21-2018

Create text table with comma(,) as field delimiter create table textcomma(age int, name string) row format delimited fields terminated by ',' stored as textfile; insert into textcomma values(1,'a'),(2,'b'),(3,'c'); Option 1 : CTAS text table with pipe(|) as field delimiter create table textpipe row format delimited fields terminated by '|' stored as textfile as select * from textcomma; # hadoop fs -cat /apps/hive/warehouse/textcomma/000000_0 1,a 2,b 3,c # hadoop fs -cat /apps/hive/warehouse/textpipe/000000_0 1|a 2|b 3|c Option 2 : Insert overwrite directory to write textfile with pipe(|) delimiter INSERT OVERWRITE DIRECTORY '/tmp/text-pipe' ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' STORED AS TEXTFILE SELECT * FROM textcomma; # hadoop fs -cat /tmp/text-pipe/000000_0 1|a 2|b 3|c If it helps to solves your query, accept the answer. It might help others.

nramanaiah · ‎09-19-2018

Can you try adding ur custom jar @ /usr/hdp/<version>/hive/auxlib/test-1.0.0.jar path on HS2 node & restart. check hive.aux.jars.path conf is having udf jar, then rerun ur query.

nramanaiah · ‎09-16-2018

Is it MR job ? what is the value for hive.execution.engine? Are you using specific queue to launch this job ? It seems to be resource unavailable issue. If AppMaster is launched, can you collect application_1536988895253_0006 logs & check why task container's are not getting launched yet.

nramanaiah · ‎09-13-2018

There are multiple ways to populate avro tables. 1) Insert into avro_table values(<col1>,<col2>..,<colN>) -- This way hive will write avro files. 2) Generating avro files & copying directly to '/tmp/staging', You can read avro documentation to write avro files directly into hdfs path. Avro Reader/Writer APIs will take care of storing & retrieving records, we don't need to explicitly specify delimiters for avro files.

nramanaiah · ‎09-12-2018

you can create external table with location & write the text files directly on that path. eg., create external table staging1(id struct<tid:string,action:string,createdts:timestamp>, cid string, anumber string) row format delimited fields terminated by ',' collection items terminated by '|' stored as textfile LOCATION '/tmp/staging/'; All text files can be directly written at /tmp/staging/ by kafka or flume If Kafka or flume will be able to generate Avro files, then you can skip the staging table & create external avro table & write avro files directly on to external table location.

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Date Registered	‎08-18-2017 06:55 AM
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