I've been trying to run some Hive queries with largish result sets. My normal approach is to submit a job through the WebHCat API, and read the results from the resulting stdout file, or to just run hive at the console and pipe stdout to a file. However, with large results (more than one reducer used), the stdout is blank or truncated.
My current solution is to create a new table from the results CREATE TABLE FROM SELECT which introduces an extra step, and leaves the table to clear up afterwards if I don't want to keep the result set.
Does anyone have a better method for capturing all the results from such a Hive query?
You can write the data directly to a directory on either hdfs or the local file system, then do what you want with the files. For example, to generate CSV files:
INSERT OVERWRITE DIRECTORY '/hive/output/folder'
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
STORED AS TEXTFILE
SELECT ... FROM ...;
This is essentially the same as CREATE TABLE FROM SELECT but you don't have to clean up the table. Here's the full documentation:
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DML#LanguageManualDML-Writingdataintothefilesystemfromqueries
Related
There are two questions:
I use unbase64() to process data and the output is completely correct in both Hive and SparkSQL. But in Presto, it shows:
Then I insert the data to both local path and hdfs, and the the data in both output files are wrong:
The code I used to insert data:
insert overwrite directory '/tmp/ssss'
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
select * from tmp_ol.aaa;
My question is:
1. Why the processed data can be shown correctly in both hive and SparkSQL but Presto? The Presto on my machine can display this kind of character.
Why the data cannot be shown correctly in the output file? The files is in utf-8 format.
You can try using CAST (AS STRING) over output of unbase64() function.
spark.sql("""Select CAST(unbase64('UsImF1dGhvcml6ZWRSZXNvdXJjZXMiOlt7Im5h') AS STRING) AS values FROM dual""").show(false)```
I'm automating a data pipeline by using a bash script to move csvs to HDFS and build external Hive tables on them. Currently, this only works when the format of the table is predefined in an .hql file. But I want to be able to read the headers from the CSV and send them as arguments to Hive. So currently I do this inside a loop through the files:
# bash
hive -S -hiveconf VAR1=$target_db -hiveconf VAR2=$filename -hiveconf VAR3=$target_folder/$filename -f create_tables.hql
Which is sent to this...
-- hive
CREATE DATABASE IF NOT EXISTS ${hiveconf:VAR1};
CREATE EXTERNAL TABLE IF NOT EXISTS ${hiveconf:VAR1}.${hiveconf:VAR2}(
individual_pkey INT,
response CHAR(1)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
LOCATION '/${hiveconf:VAR3}'
I want the hive script to look more like this...
CREATE DATABASE IF NOT EXISTS ${hiveconf:VAR1};
CREATE EXTERNAL TABLE IF NOT EXISTS ${hiveconf:VAR1}.${hiveconf:VAR2}(
${hiveconf:ROW1} ${hiveconf:TYPE1},
... ...
${hiveconf:ROW_N} ${hiveconf:TYPE_N}
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
LOCATION '/${hiveconf:VAR3}'
Is it possible to send it some kind of array that it would parse? Is this feasible or advisable?
I eventually figured out a way around this.
You can't really write an HQL script that takes in a variable number of fields. You can, however, write a bash script that generates an HQL script of variable length. I've implemented this for my team, but the general idea is to write out how you want the HQL to look as a string in bash, then use something like Rscript to read in and identify the data types of your CSV. Store the data types as an array along with the CSV headers and then loop through those arrays, writing the information to the HQL.
In hive, how can I delete duplicate records ? Below is my case,
First, I load data from product table to products_rcfileformat. There are 25 rows of records on product table
FROM products INSERT OVERWRITE TABLE products_rcfileformat
SELECT *;
Second, I load data from product table to products_rcfileformat. There are 25 rows of records on product table. But this time I'm NOT using OVERWRITE clause
FROM products INSERT INTO TABLE products_rcfileformat
SELECT *;
When I query the data it give me total rows = 50 which are right
Check from hdfs, it seem hdfs make another copy of file xxx_copy_1 instead of append to 000000_0
Now I want to remove those records that read from xxx_copy_1. How can I achieve this in hive command ? If I'm not mistaken, i can remove xxx_copy_1 file by using hdfs dfs -rm command follow by rerun insert overwrite command. But I want to know whether this can it be done by using hive command example like delete statement?
Partition your data such that the rows (use window function row_number) you want to drop are in a partition unto themselves. You can then drop the partition without impacting the rest of your table. This is a fairly sustainable model, even if your dataset grows quite large.
detail about Partition .
www.tutorialspoint.com/hive/hive_partitioning.htm
Check from hdfs, it seem hdfs make another copy of file xxx_copy_1
instead of append to 000000_0
The reason is hdfs is read only, not editable, as hive warehouse files (or whatever may be the location) that is still in hdfs, so it has to create a second file.
Now I want to remove those records that read from xxx_copy_1. How can
I achieve this in hive command ?
Please check this post - Removing DUPLICATE rows in hive based on columns.
Let me know if you are satisfied with the answer there. I have another method, which removes duplicate entries but may not be in the way you want.
I completed my hadoop course now I want to work on Hadoop. I want to know the workflow from data ingestion to visualize the data.
I am aware of how eco system components work and I have built hadoop cluster with 8 datanodes and 1 namenode:
1 namenode --Resourcemanager,Namenode,secondarynamenode,hive
8 datanodes--datanode,Nodemanager
I want to know the following things:
I got data .tar structured files and first 4 lines have got description.how to process this type of data im little bit confused.
1.a Can I directly process the data as these are tar files.if its yes how to remove the data in the first four lines should I need to untar and remove the first 4 lines
1.b and I want to process this data using hive.
Please suggest me how to do that.
Thanks in advance.
Can I directly process the data as these are tar files.
Yes, see the below solution.
if yes, how to remove the data in the first four lines
Starting Hive v0.13.0, There is a table property, tblproperties ("skip.header.line.count"="1") while creating a table to tell Hive the number of rows to ignore. To ignore first four lines - tblproperties ("skip.header.line.count"="4")
CREATE TABLE raw (line STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' LINES TERMINATED BY '\n';
CREATE TABLE raw_sequence (line STRING)
STORED AS SEQUENCEFILE
tblproperties("skip.header.line.count"="4");
LOAD DATA LOCAL INPATH '/tmp/test.tar' INTO TABLE raw;
SET hive.exec.compress.output=true;
SET io.seqfile.compression.type=BLOCK; -- NONE/RECORD/BLOCK (see below)
INSERT OVERWRITE TABLE raw_sequence SELECT * FROM raw;
To view the data:
select * from raw_sequence
Reference: Compressed Data Storage
Follow the below steps to achieve your goal:
Copy the data(ie.tar file) to the client system where hadoop is installed.
Untar the file and manually remove the description and save it in local.
Create the metadata(i.e table) in hive based on the description.
Eg: If the description contains emp_id,emp_no,etc.,then create table in hive using this information and also make note of field separator used in the data file and use the corresponding field separator in create table query. Assumed that file contains two columns which is separated by comma then below is the syntax to create the table in hive.
Create table tablename (emp_id int, emp_no int)
Row Format Delimited
Fields Terminated by ','
Since, data is in structured format, you can load the data into hive table using the below command.
LOAD DATA LOCAL INPATH '/LOCALFILEPATH' INTO TABLE TABLENAME.
Now, local data will be moved to hdfs and loaded into hive table.
Finally, you can query the hive table using SELECT * FROM TABLENAME;
I have a design question where in in my CDH 4.1.2(Cloudera) installation I have daily rolling log data dumped into the HDFS. I have some reports to calculate the success and failure rates per day.
I have two approaches
load the daily log data into Hive Tables and create a complex query.
Run a MapReduce job upfront everyday to generate the summary (which
is essentially few lines) and keep appending to a common file which is a Hive Table. Later while running the report I could use a simple select query to fetch the summary.
I am trying to understand which would be a better approach among the two or if there is a better one.
The second approach adds some complexity in terms of merging files. If not merged I would have lots of very small files which seems to be a bad idea.
Your inputs are appreciated.
Thanks
Hive seems well suited to this kind of tasks, and it should be fairly simple to do:
Create an EXTERNAL table in Hive which should be partitioned by day. The goal is that the directory where you will dump your data will be directly in your Hive table. You can specify the delimiter of the fields in your daily logs like shown below where I use commas:
create external table mytable(...) partitioned by (day string) row format delimited keys terminated by ',' location '/user/hive/warehouse/mytable`
When you dump your data in HDFS, make sure you dump it on the same directory with day= so it can be recognized as a hive partition. For example in /user/hive/warehouse/mytable/day=2013-01-23.
You need then to let Hive know that this table has a new partition:
alter table mytable add partition (day='2013-01-23')
Now the Hive metastore knows about your partition, you can run your summary query. Make sure you're only querying the partition by specifying ... where day='2013-01-23'
You could easily script that to run daily in cron or something else and get the current date (for example with the shell date command) and substitute variables to this in a shell script doing the steps above.