irvflor nazaire firmin
Kylee Evancho
Hive partitioning is a partitioning strategy that is used to split a table into multiple files based on partition keys. The files are organized into folders. Within each folder, the partition key has a value that is determined by the name of the folder.
Filters on the partition keys are automatically pushed down into the files. This way the system skips reading files that are not necessary to answer a query. For example, consider the following query on the above dataset:
By default the system tries to infer if the provided files are in a hive partitioned hierarchy. And if so, the hive_partitioning flag is enabled automatically. The autodetection will look at the names of the folders and search for a 'key' = 'value' pattern. This behaviour can be overridden by setting the hive_partitioning flag manually.
I have a 10 GB file with a date field and an hour of day field. Can I load this file into a table, then insert-overwrite into another partitioned table that uses those fields as a partition? Would something like the following work?
I just ran across this trying to answer the same question and it was helpful but not quite complete. The short answer is yes, something like the query in the question will work but the syntax is not quite right.
Columns a and b are just some example columns. dt and hour are the values we want to partition on once it gets to the production table. Moving the staging data to production from staging_unpartitioned and staging_partitioned looks exactly the same.
This uses a process called Dynamic Partitioning which you can read about here. The important thing to note is that which columns are associated with which partitions is determined by the SELECT order. All dynamic partitions must be selected last and in order.
Then you might hit an error if you aren't partitioning on at least one static partition before the dynamic partitions. This restriction would save you accidentally removing a root partition when you meant to overwrite its sub-partitions with dynamic partitions. In my experience this behavior has never been helpful and has often been annoying, but your mileage may vary. At any rate, it is easy to change:
Maybe this is already answered... but yes, you can do exactly as you have stated. I have done it many times. Obviously your new table would need to be defined similar to the original one, but without the partition column, and with the partition specification. Also, I cannot remember if I had to explicitly list out the columns in the original table, or if the asterik was sufficient.
Hive partitions are used to split the larger table into several smaller parts based on one or multiple columns (partition key, for example, date, state e.t.c). The hive partition is similar to table partitioning available in SQL server or any other RDBMS database tables.
Hive table partition is a way to split a large table into smaller logical tables based on one or more partition keys. These smaller logical tables are not visible to users and users still access the data from just one table.
As data is loaded into the partitioned table, Hive internally divides the records based on the partition key. It then stores the data for each partition in a sub-directory within the tables directory on HDFS. The directory is named after the partition key and its corresponding value.
I am facing a problem with hive default partition (null partition) in hive.I will explain the situation briefly here.. I have a hive main table and data ingestion is happening to that table everyday. but somehow when data is ingested into the hive table something went wrong and partition is showing _hive_default_partition_ or in my understanding it is null partition.
To query externally partitioned data,you must create aBigLake tableor anexternal table.We recommend using BigLake tables because they let you enforcefine-grained security at the table level.For information about BigLake and external tables, seeIntroduction to BigLake tablesand Introduction to external tables.
To use a CUSTOM schema, you must specify the schema in the source URI prefixfield. Using a CUSTOM schema lets you specify the type for each partition key.The values must validly parse as the specified type or the query fails.
When this option is enabled, attempts to query the externally partitioned tablewithout specifying a WHERE clause produce the following error:Cannot query over table without a filter over column(s) that can be used for partition elimination.
Note: There must be at least one predicate thatonly references one or more partition keys for the filter to be consideredeligible for partition elimination. For example, for a table with partition keyval and column f in the file, both of the followingWHERE clauses satisfy the requirement:
WHERE val = "key"
WHERE val = "key" AND f = "column"However, WHERE (val = "key" OR f = "column") is not sufficient.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
i have uploaded two files with same partitions with different data in gap of 1 hours, both files are processed successfully and logs showing that partitions are adding drooped properly, but while checking the data in table, data was not there.
Hello @rama
Are you issuing the "alter table [table name] add partition [partition name and value]" command?
When dealing with external tables (or manually adding data to a managed table.. i.e. adding a file on hdfs), you need to manually tell hive that there's a new partition.
Paul's suggestion to running "msck repair table" triggers a automatic partition discovery.
I would suggest :
1-When adding a new partition, issue the above alter table statement.
2-If you forgot to do step #1, issue a msck command to recover all missing partitions.
Makes sense?
How much is set for hive.exec.max.dynamic.partitions?
BTW, could you check if your hive log is showing smtg related to lock?
If it is, try to unlock your tables (sintax e.g. -> UNLOCK TABLE ).
And one last thing, just asking, but are you using External tables? And you're running a MSCK REPAIR TABLE after each batch?
no i have not running MSCK repair table after my batch load this is second time i am facing this issue in row, i resolved this issue by reload the files again, once i reload file all partitions are properly added.but i worried about recurring this issue.
Please note one point i forgot to mention, after i load the file it will check the if partitions are existed or not if partitioned already exist it will drop the old one and add the new parititons, BUT here the complete partitions are not visible.
EX: I have partitions based on key and month, i have keys A,B,C and months 01,02,03 and if i load a new file with key=A and month=03 , it is drooping only and key=A and month=03 and adding the new partitions but after process complete i could not see data for entire Key=A for all months in hive tables. After some time if reload the file , i could see entire data.
Hm. that's strange.
By any chance to have the add partition + insert in a short period between them?
I'm asking this, cause I'm suspecting 2 things:
- That you had your partition added and somehow the table got locked. You can check this by running show locks;
- Check if your HiveMetaStore DB (mysql, derby or etc) is healthy.
So guess, in the next time you can try to do the following:
- Enable DEBUG mode for HiveMetastore logs and check if you find something.
- Login into the DB and check if your partitions have been added properly
- Login into Hive with verbose and run SHOW LOCKS;
- Just to confirm, make sure that you're running the msck repair table ; after the whole process ended.
Hope this helps!
Hive is designed to enable easy data summarization, ad-hoc querying and analysis of large volumes of data. It provides SQL which enables users to do ad-hoc querying, summarization and data analysis easily. At the same time, Hive's SQL gives users multiple places to integrate their own functionality to do custom analysis, such as User Defined Functions (UDFs).
In the following sections we provide a tutorial on the capabilities of the system. We start by describing the concepts of data types, tables, and partitions (which are very similar to what you would find in a traditional relational DBMS) and then illustrate the capabilities of Hive with the help of some examples.
Note that it is not necessary for tables to be partitioned or bucketed, but these abstractions allow the system to prune large quantities of data during query processing, resulting in faster query execution.
This type hierarchy defines how the types are implicitly converted in the query language. Implicit conversion is allowed for types from child to an ancestor. So when a query expression expects type1 and the data is of type2, type2 is implicitly converted to type1 if type1 is an ancestor of type2 in the type hierarchy. Note that the type hierarchy allows the implicit conversion of STRING to DOUBLE.
Java's "LocalDateTime" timestamps record a date and time as year, month, date, hour, minute, and seconds without a timezone. These timestamps always have those same values regardless of the local time zone.
For example, the timestamp value of "2014-12-12 12:34:56" is decomposed into year, month, day, hour, minute and seconds fields, but with no time zone information available. It does not correspond to any specific instant. It will always be the same value regardless of the local time zone. Unless your application uses UTC consistently, timestamp with local time zone is strongly preferred over timestamp for most applications. When users say an event is at 10:00, it is always in reference to a certain timezone and means a point in time, rather than 10:00 in an arbitrary time zone.
c01484d022