Analytics/Systems/Cluster/Hive/Queries

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Prerequisites

  1. You have a Wikitech/LDAP account – see Analytics/Onboarding#Wikitech/Cloud_VPS
  2. You have requested access to analytics-privatedata-users or analytics-users.
    1. You created a task like phab:T96053
    2. Your manager has approved your access request in the task you created above
    3. Ops has fulfilled the request
  3. You have ssh configured to tunnel through Bastion – see Analytics/Onboarding#Sample ssh config
  4. You can start a shell on stat1004 by entering ssh stat1004.eqiad.wmnet in a command-line terminal.

Running queries

Note: The Hive client that is launched by using hivecli, is being deprecated, and is being replaced with beelineinterface for HiveServer2 instead. Querying works the same way as this tutorial will explain, but you can use beeline in place of hive. See Analytics/Cluster/Beeline for full usage documentation.

Once you can ssh to stat1004 you can simply access the hive command-line interface by entering hive. Here's the start of a sample interactive session:

 nuria@stat1004:~$ beeline

0: jdbc:hive2://analytics1003.eqiad.wmnet:100> show databases;
 wmf
 ...
0: jdbc:hive2://analytics1003.eqiad.wmnet:100> use wmf;
No rows affected (0.019 seconds)

0: jdbc:hive2://analytics1003.eqiad.wmnet:100> show tables;
tab_name
aqs_hourly
browser_general
last_access_uniques_daily
last_access_uniques_monthly
mediacounts
mobile_apps_session_metrics
mobile_apps_session_metrics_by_os
mobile_apps_uniques_daily
mobile_apps_uniques_monthly
pagecounts_all_sites
pageview_hourly
pageview_unexpected_values
pageview_whitelist
projectview_hourly
webrequest
webrequest_empty
16 rows selected (0.028 seconds)

You can also write your query in a text file, e.g. named my_query.hql, and execute it. This example redirects the output of the query (by default in tab-separated values format) to results.txt:

 $ beeline -f my_query.hql > results.txt


Use wmf

You should use wmf database (instead of the wmf_raw database) if you can, or your queries will be slow. The wmf database includes filtered and preprocessed data.

Output to a file

beeline -f my_query.hql > out.txt 

Run queries in Cron

# The USER variable needs to be set with the username
# that will run the queries. Example: USER=elukey
USER=<user> /usr/local/bin/beeline -f my_query.hql > out.txt

Always restrict queries to a date range (partitioning)

There is a lot of data in Hive, so you want to make sure your queries have time bounds so they complete in a reasonable amount of time. For most tables you will be copying and pasting

WHERE year=2017 AND month=12 AND day=13

to all your queries, and maybe appending AND hour=NN as well.

In fact, you must restrict your query to some Hive "partition", otherwise Hive will fail with the error "No partition predicate found". The partitioning of most tables is by year/month/day/hour. For example, seeing this simple query fail:

0: jdbc:hive2://analytics1003.eqiad.wmnet:100> SELECT agent_type FROM webrequest LIMIT 5;
Error: Error while compiling statement: FAILED: SemanticException [Error 10041]: No partition predicate found for Alias "webrequest" Table "webrequest" (state=42000,code=10041)

you must restrict it, for example:

0: jdbc:hive2://analytics1003.eqiad.wmnet:100> SELECT agent_type FROM webrequest WHERE webrequest_source='misc' AND year=2016 AND month=4 AND day=29 and hour=0 LIMIT 5;
agent_type
user
user
user
spider
user

Run long queries in a screen session and in the nice queue

  • Screen: If your queries take a long time to run you can execute them in a screen session. If you are accidentally disconnected, or you intentionally disconnect, from a screen session, your remote shell and queries continue to run.
  • Nice queue: For long queries, it's good practice to let fast queries take precedence for resources on the cluster. In order to do that, long queries should be run in the nice yarn queue. You can do so by
    • In hive CLI, execute the command: SET mapred.job.queue.name=nice;
    • In batch mode, add a parameter: hive -- hiveconf mapred.job.queue.name=nice ...

User-defined functions

You can create or reuse UDFs (user-defined functions), see Analytics/Cluster/Hive/QueryUsingUDF. WMF has developed several UDFs, such as is_wikimedia_bot, geocoded_data, etc. Some are run to create its derived tables from raw Hadoop information.

Sample queries

Here are sample queries from the tables in the wmf database. Run hive and enter use wmf; before running these.

Pageviews

Using the pre-aggregated table pageview_hourly

SELECT
  concat(month,'/',day,'/',year), access_method, sum(view_count)
FROM 
  wmf.pageview_hourly
WHERE
  year = 2015
  AND month = 8
  AND agent_type = "user"
  AND country = "Canada"
  AND project = "en.wikipedia"
GROUP BY
  year, month, day, access_method;

Distinct IPs

SELECT
  COUNT(DISTINCT ip) AS hits
FROM
  webrequest
WHERE
  year = 2014
  AND month = 5
  AND day = 5
  AND hour = 13
  AND uri_host = "en.wikipedia.org"
  AND http_status = 200;

Top raw referrals for an article

SELECT
  referer,
  COUNT(DISTINCT ip) AS hits
FROM
  webrequest
WHERE
  year = 2014
  AND month = 5
  AND day = 5
  AND hour = 13
  AND uri_path = "/wiki/London"
  AND uri_host = "en.wikipedia.org"
  AND http_status = 200
GROUP BY referer
ORDER BY hits DESC
LIMIT 50;

Top internal referrals for an article

SELECT
  SUBSTR(referer,30) AS source,
  COUNT(DISTINCT ip) AS hits
FROM
  webrequest
WHERE
  year = 2014
  AND month = 5
  AND day = 5
  AND hour = 13
  AND uri_path = "/wiki/London"
  AND uri_host = "en.wikipedia.org"
  AND referer LIKE "http://en.wikipedia.org/wiki/%"
  AND http_status = 200
GROUP BY
  SUBSTR(referer,30)
ORDER BY hits DESC
LIMIT 50;

Top outbound clicks from an article

SELECT
  SUBSTR(uri_path,7) AS target,
  COUNT(DISTINCT ip) AS hits
FROM
  webrequest
WHERE
  year = 2014
  AND month = 5
  AND day = 5
  AND hour = 13
  AND uri_host = "en.wikipedia.org"
  AND referer LIKE "http://en.wikipedia.org/wiki/London%"
AND 
  http_status = 200
GROUP BY SUBSTR(uri_path,7)
ORDER BY hits
DESC LIMIT 50;

FAQ

JsonSerDe Errors

If you get errors like:

FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. java.lang.ClassNotFoundException Class org.apache.hive.hcatalog.data.JsonSerDe not found

then enter the following:

ADD JAR /usr/lib/hive-hcatalog/share/hcatalog/hive-hcatalog-core.jar ;

Killing a running query

Once you submit a query, it is handed off to Hadoop. Hadoop runs the query as a YARN application. The Hive CLI is then detached from the actual application. If you Ctrl-C your Hive CLI, you will quit the interface you used to submit the query, but will not actually kill the application. To kill the application, you have to tell YARN you want it dead.

Note the application ID from when your query started. You should see something like:

 Starting Job = job_1387838787660_12241, Tracking URL = http://analytics1010.eqiad.wmnet:8088/proxy/application_1387838787660_12241/

The application ID in this case is application_1387838787660_12241. To kill this application, run:

 yarn application -kill application_1387838787660_12241

Slow queries

A simple SELECT LIMIT like the following will prepare all data from a partition before applying the LIMIT clause, so it's always best to specify the lowest-level relevant partition for the query.

SELECT http_status,uri_path FROM webrequest WHERE year = 2014 LIMIT 10;

Scratch space

You can create your own database using regular SQL

CREATE DATABASE dartar;

Batch mode

You can save the output of a hive query into a TSV by using hive in batch mode, e.g.

hive  -e "USE wmf; SELECT foo;" > ./bar.tsv

Dates

Dates are expressed as integers due to how we have configured this data. You may encounter this error if you treat dates as strings by quoting:

> Diagnostic Messages for this Task:
> Error: java.io.IOException: java.lang.reflect.InvocationTargetException
>       [...]
> Caused by: java.lang.reflect.InvocationTargetException
>       [...]
> Caused by: java.io.EOFException
>       [...]

Out of Memory Errors on Client

When performing large queries, the hive client may run out of memory. Just look for an out of memory error at the top of the stack trace.

Invoke Hive via the following to allocate more memory

 export HADOOP_HEAPSIZE=2048 && beeline

Out of Memory Errors when exporting large datasets

If your query generates a large dataset and you are writing it out to a file (e.g. beeline -f query.hql > output.tsv), your query might fail due to beeline buffering all the data before writing it out, thereby running out of memory. Passing the parameter --incremental=true to make beeline print to stdout incrementally as data comes in. The previous example turns into the following command:

beeline --incremental=true -f query.hql > output.tsv

Another solution is of course to switch to a data processing approach that does not require the data to be exported.

Killing Jobs

Standard Hadoop practice involves launching a long running job and then realizing you forgot a parameter. Here is how you kill a job from the command line:

yarn application -kill [applicationID]

Your application id can be found in the hive messages -- it's the application_XXX string at the end of the tracking URL:

Starting Job = job_1409078537822_2754, Tracking URL = http://analytics1001.eqiad.wmnet:8088/proxy/application_1409078537822_2754/

INSERT OVERWRITE ... IF NOT EXISTS

When using INSERT OVERWRITE with IF NOT EXISTS be sure to use unqualified tables. Otherwise, the IF NOT EXISTS gets silently ignored.

Earliest data available in hadoop

What’s the most efficient way to obtain the earliest, non-truncated hour or date of data still available in hadoop?

  
show partitions webrequest;

Will show you all of the available partitions. Webrequest data in Hadoop is currently pruned after 62 days.

Avoiding overgreedy scans / Operator precedence

In HiveQL, AND takes precedence over OR.

So when trying to select all “mobile” and “text” partitions for 2015-01-01,

  
  ... WHERE year=2015 AND month=1 AND day=1 AND webrequest_source='mobile' OR webrequest_source='text'

would not do the trick, as Hive parses it as

  
  ... WHERE (year=2015 AND month=1 AND day=1 AND webrequest_source='mobile') OR webrequest_source='text'

and would select all “text” partitions. This unintentionally skews the data you want to obtain, makes the query take longer, and keeps all “text” partitions locked.

You can use parenthesis to override operator precedence. So for example

  
  ... WHERE year=2015 AND month=1 AND day=1 AND (webrequest_source='mobile' OR webrequest_source='text')

would select the “mobile” and “text” partitions for 2015-01-01.

My query does not start

Check if the cluster is overloaded. If it is, let the Analytics team know.

My query does not progress / finish

Querying through Hive is not as fast as querying a plain SQL table. So expect to wait an hour for your result to show up.

But if the progress counter in your query does not increase (like at least a 1% per minute), you are either unintentionally querying a lot of data, or the cluster is stalled.

If you are querying “a lot” of data (a week worth of webrequest logs is definitely already more “a lot”!), consider splitting up your query. That will increase performance for you, cut down runtime of your query, and it will leave cluster resources available to other users. If you run into blockers, let the Analytics team know.

If your query is small, nice and neat, and does not progress nonetheless, please check if the cluster is overloaded. If it is, let the Analytics team know.


Search through logs

If your job is finished, you can find all of the job logs in HDFS at:

/mnt/hdfs/var/log/hadoop-yarn/apps/<user>/

Where USER is your username (echo $USER at a shell prompt) and application_id is the application_XXXXXX_XXXX number that yarn gives you when your app starts.

You can also access these via the yarn CLI, like:

 yarn logs -applicationId <applicationId>

More debgugging information in CLI

You can get more debugging information in your CLI by launching Hive with a special configuration option:

hive  -hiveconf hive.root.logger=INFO,console

Vertical query results

!set outputformat vertical