I have a number of hive warehouses. The data resides in parquet files in Amazon S3. Some of the tables contain TB of data. Currently in hive most tables are partitioned by a combination of month and year, both of which are saved mainly as string. Other fields are either bigint, int, float, double, string and unix timestamps. Our goal is to migrate them to apache iceberg tables. The challenge is how to choose the partition keys.
I have already calculated the cardinality of each field in each table by:
Select COUNT(DISTINCT my_column) As my_column_count
From my_table;
I have also calculated the percentage of null values for each field:
SELECT 100.0 * count(*)/number_of_all_records
FROM my_db.my_table
Where my_column IS NULL;
In short I already know three things for each field:
Data type
Cardinality
Percentage of null values
By knowing these three pieces of information, my question is how should I choose the best column or combination of columns as partition keys for my future iceberg tables? Are there any rule of thumbs?
How many partitions is considered as optimized when choosing partition keys? What data type is best when choosing partition keys? What are other factors that need to be considered? Is bucketing the same in iceberg tables as it is in hive and how it can be leveraged by the partition keys? What data types are best for partition keys? Is it better to have many small partitions or having a few big partitions? Any other aspects in partition keys that need to be considered?
One crucial part is missing from your description - the queries. You need to understand what are the queries that will run on this data. Understanding the queries that will run on the data (to the best you can) is super important.
For example, consider a simple table with: Date, Id, Name, Age as columns.
If the queries are date based meaning, it will query the data in the context of dates,
select * from table where date > 'some-date'
then it's a good idea to partition by date.
However, if the queries are age related
select * from table where age between 20 and 30
then you should consider partition by age or age groups
I want to create such table:
CREATE TABLE sometable
(SELECT columns, columns, date_col)
PARTITIONED BY date_col
And I want it to be date partitioned with the date in table suffix: sometable$date_partition
I read the docs, but can't complete this neither with web UI nor with SQL.
The web UI shows such error "Missing argument for parameter DATE."
My table name is "daily_export_${DATE}"
My partitioning column isn't blank, it's date_col.
Can I have a simple example, please?
PARTITION BY goes earlier
The query needs to parse the table suffix into a DATE type.
For example:
CREATE OR REPLACE TABLE temp.so
PARTITION BY date_from_table_name
AS
SELECT PARSE_DATE('%Y%m%d', _table_suffix) date_from_table_name, event_timestamp, event_name, items
FROM `bingo-blast-174dd.analytics_151321511.events_*`
WHERE _table_suffix BETWEEN '20200530' AND '20200531'
LIMIT 10
As you can see in this documentation, BigQuery implements two different concepts: sharded tables and partitioned tables
The first one (sharded tables) is a way of dividing a whole table into many tables with a date suffix. You can query those tables individually or using wildcards. For example, instead of creating a single table named events, you can create many tables named events_20200101, events_20200102, [...]
When you do that, you are able to query any of those tables individually or you can query all of them by running some query like select * from events_*
The second concept (partitioned tables) is an approach to fragment your table in smaller pieces in order to improve the performance and reduce costs when querying data. Partitioned tables can be based on some column of your table or even on the ingestion time. When you table is partitioned by ingestion time you can access a pseudo column named _PARTITIONTIME
When comparing both approaches, the documentation says:
Date/timestamp partitioned tables perform better than tables sharded
by date. When you create date-named tables, BigQuery must maintain a
copy of the schema and metadata for each date-named table. Also, when
date-named tables are used, BigQuery might be required to verify
permissions for each queried table. This practice also adds to query
overhead and impacts query performance. The recommended best practice
is to use date/timestamp partitioned tables instead of date-sharded
tables.
In your case, you basically need to create a partitioned table without a date in its name.
I have come across Hive tables which I need to convert to Redshift/MySql equivalent.
I am having trouble understanding Hive query structure and would appreciate some help:
CREATE TABLE IF NOT EXISTS table_1 (
id BIGINT,
price DOUBLE,
asset string
)
PARTITIONED BY (
pt STRING
);
ALTER TABLE table_1 DROP IF EXISTS PARTITION (pt== '${yyyymmdd}');
INSERT OVERWRITE TABLE table_1 PARTITION (pt= '${yyyymmdd}')
select aa.id,aa.price,aa.symbol from
...
...
from
table_2 table
I am having trouble understanding the PARTITIONED BY clause. This, if I am understanding correctly, is different from MySQL table partitions, and is a Hive specific dynamic partition.
The partition does not define a column or a key, and partitions by the current date.
Does this mean that table_1 is partitioned by the date? Each day has a separate partition?
Then later on in the code there are notations similar to
inner join table_new table on table.pt = '${yyyymmdd}' and ...
In this context, does it mean only rows inserted on yyyymmdd are selected for the join?
Thank you.
Partition in Hive is a folder in HDFS by default with name key=value + metadata in the Hive metastore. You can alter partition location and create partition on top of any folder.
This PARTITIONED BY (pt STRING) defines partition column pt of type string, not date. Partition values are stored in the metadata. The pt column is not present in the table data files, it is only defined in PARTITIONED BY, all partition values are stored in the metadata. If you load partition dynamically, partition folder is being created with name pt='value'.
This sentence creates partition dynamically:
INSERT OVERWRITE TABLE table_1 PARTITION (pt)
select id, price, symbol
coln as pt --partition column should be the last one
from ...
And this sentence loads single STATIC partition:
INSERT OVERWRITE TABLE table_1 PARTITION (pt= '${yyyymmdd}')
select aa.id,aa.price,aa.symbol
from
No partition column is selected, partition value specified in the
PARTITION (pt= '${yyyymmdd}')
'${yyyymmdd}' here is a parameter with name yyyymmdd which is passed to the script using --hivevar like this:
hive --hivevar yyyymmdd=20200604 -f myscript.sql
You can pass ANY string as partition value in this case, though parameter name yyyymmdd suggests it's format.
BTW date format in hive is 'yyyy-MM-dd' Strings in 'yyyy-MM-dd' format can be implicitly converted to DATE.
I will try in one shot explain what is partitioning in Hive. First of all would be
WHEN TO USE TABLE PARTITIONING
Table partitioninig is good when:
Reading the entire dataset takes too long
Queries almost always filter on the partition columns
There are a reasonable number of different values for partition columns
Data generation of ETL process splits data by file or directory names
Partition column values are not in the data itself
Don't partition on columns with many unique values
Example: Partitioning customers by first name
CREATING PARTITIONED TABLES
To create a partitioned table, use the PARTITIONED BY clause in the CREATE TABLE statement.
The names and types of the partition columns must be specified
in the PARTITIONED BY clause, and only in the PARTITIONED BY clause.
They must not also appear in the list of all the other columns.
CREATE TABLE customers_by_country
(cust_id STRING, name STRING)
PARTITIONED BY (country STRING)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';
The example CREATE TABLE statement shown above creates the table customers_by_country,
which is partitioned by the STRING column named country.
Notice that the country column appears only in the PARTITIONED BY clause,
and not in the column list above it.
This example specifies only one partition column, but you can specify more than one by using
a comma-separated column list in the PARTITIONED BY clause.
Aside from these specific differences, this CREATE TABLE statement is the same
as the statement used to create an equivalent non-partitioned table.
Table partitioning is implemented in a way that is mostly transparent
to a user issuing queries with Hive.
A partition column is what’s known as a virtual column, because its values are not stored within the data files.
Following is the result of the DESCRIBE command on customers_by_country;
it displays the partition column country just as if it were a normal column within the table.
You can refer to partition columns in any of the usual clauses of a SELECT statement.
name type comment
cust_id string
name string
country string
You can load data in partitioned tables dynamically or statically
LOADING DATA WITH DYNAMIC PARTITION
One way to load data into a partitioned table is to use dynamic partitioning,
which automatically defines partitions when you load the data, using the values in the partition column.
(The other way is to manually define the partitions with Static Partitioning)
To use dynamic partitioning, you must load data using an INSERT statement.
In the INSERT statement, you must use the PARTITION clause to list the partition columns.
The data you are inserting must include values for the partition columns.
The partition columns must be the rightmost columns in the data you are inserting,
and they must be in the same order as they appear in the PARTITION clause.
INSERT OVERWRITE TABLE customers_by_country
PARTITION(country)
SELECT cust_id, name, country FROM customers;
The example shown above uses an INSERT … SELECT statement
to load data into the customers_by_country table with dynamic partitioning.
Notice that the partition column, country, is included
in the PARTITION clause and is specified last in the SELECT list.
When Hive executes this statement, it automatically creates partitions
for the country column and loads the data into these partitions based on the values in the country column.
The resulting data files in the partition subdirectories do not include values for the country column.
Since the country is known based on which subdirectory a data file is in,
it would be redundant to include country values in the data files as well.
Look at the contents of the customers_by_country directory.
It should now have one subdirectory for each value in the country column.
Look at the file in one of those directories.
Notice that the file contains the row for the customer from that country,
and no others; notice also that the country value is not included.
Note: Hive includes a safety feature that prevents users
from accidentally creating or overwriting a large number of partitions.
(See “Risks of Using Partitioning” for more about this.)
By default, Hive sets the property hive.exec.dynamic.partition.mode to strict.
This prevents you from using dynamic partitioning, though you can still use static partitions.
You can disable this safety feature in Hive by setting
the property hive.exec.dynamic.partition.mode to nonstrict:
SET hive.exec.dynamic.partition.mode=nonstrict;
Then you can use the INSERT statement to load the data dynamically.
Hive properties set in Beeline are for the current session only,
so the next time you start a Hive session this property will be set back to strict.
But you or your system administrator can configure properties permanently, if necessary.
When you run some SELECT queries on the partitioned table, if the table is big enough you can note significant difference in the time it takes to run.
Notice that you will not query the table any differently than you would query the customers table.
LOADING DATA WITH STATIC PARTITIONING
One way to load data into a partitioned table is to use static partitioning,
in which you manually define the different partitions.
With static partitioning, you create a partition manually, using an ALTER TABLE … ADD PARTITION statement,
and then load the data into the partition.
For example, this ALTER TABLE statement creates the partition for Pakistan (pk):
ALTER TABLE customers_by_country
ADD PARTITION (country='pk');
Notice how the partition column name, which is country, and the specific value that defines this partition,
which is pk, are both specified in the ADD PARTITION clause.
This creates a partition directory named country=pk inside the customers_by_country table directory.
After the partition for Pakistan is created, you can add data into the partition using an INSERT … SELECT statement:
INSERT OVERWRITE TABLE customers_by_country
PARTITION(country='pk')
SELECT cust_id, name FROM customers WHERE country='pk'
Notice how in the PARTITION clause, the partition column name, which is country,
and the specific value, which is pk, are both specified, just like in the ADD PARTITION command used to create the partition.
Also notice that in the SELECT statement, the partition column is not included in the SELECT list.
Finally, notice that the WHERE clause in the SELECT statement selects only customers from Pakistan.
With static partitioning, you need to repeat these two steps for each partition:
first create the partition, then add data.
You can actually use any method to load the data; you need not use an INSERT statement.
You could instead use hdfs dfs commands or a LOAD DATA INPATH command.
But however you load the data, it’s your responsibility to ensure that data is stored in the correct partition subdirectories.
For example, data for customers in Pakistan must be stored in the Pakistan partition subdirectory,
and data for customers in other countries must be stored in those countries’ partition subdirectories.
Static partitioning is most useful when the data being loaded
into the table is already divided into files based on the partition column,
or when the data grows in a manner that coincides with the partition column:
For example, suppose your company opens a new store in a different country,
like New Zealand ('nz'), and you're given a file of data for new customers, all from that country.
You could easily add a new partition and load that file into it.
RISKS OF USING PARTITIONING
A major risk when using partitioning is creating partitions that lead you into the small files problem.
When this happens, partitioning a table will actually worsen query performance
(the opposite of the goal when using partitioning) because it causes too many small files to be created.
This is more likely when using dynamic partitioning, but it could still
happen with static partitioning—for example if you added a new partition to a sales table
on a daily basis containing the sales from the previous day,
and each day’s data is not particularly big.
When choosing your partitions, you want to strike a happy balance between too many partitions
(causing the small files problem) and too few partitions (providing performance little benefit).
The partition column or columns should have a reasonable number of values
for the partitions—but what you should consider reasonable is difficult to quantify.
Using dynamic partitioning is particularly dangerous because if you're not careful,
it's easy to partition on a column with too many distinct values.
Imagine a use case where you are often looking for data that falls within
a time frame that you would specify in your query.
You might think that it's a good idea to partition on a column that pertains to time.
But a TIMESTAMP column could have the time to the nanosecond, so every row could have a unique value;
that would be a terrible choice for a partition column! Even to the minute or hour could create
far too many partitions, depending on the nature of your data;
partitioning by larger time units like day, month, or even year might be a better choice.
As another example, consider an employees table.
This has five columns: empl_id, first_name, last_name, salary, and office_id.
Before reading on, think for a moment, which of these might be reasonable for partitioning
The column empl_id is a unique identifier.
If that were your partition column, you would have a separate partition for each employee,
and each would have exactly one row.
In addition, it's not likely you'll be doing a lot of queries looking for a particular value,
or even a particular range of values. This is a poor choice.
The column first_name will not have one per employee, but there will likely be many columns that have only one row.
This is also true for last_name.
Also, like empl_id, it's not likely you'll need filter queries based on these columns. These are also poor choices.
The column salary also will have many divisions
(and even more so if your salaries go to the cent rather than to the dollar as our sample table does).
While it may be that you'll sometimes want to query on salary ranges,
it's not likely you'll want to use individual salaries.
So salary is a poor choice.
A more limited salary_grades specification, like the ones in the salary_grades table,
might be reasonable if your use case involves looking at the data by salary grade frequently.
The office_id column identifies the office where an employee works.
This will have a much smaller number of unique values, even if you have a large company with offices in many cities.
It's imaginable that your use case might be to frequently filter
your employee data based on office location, too. So this would be a good choice.
You also can use multiple columns and create nested partitions.
For example, a dataset of customers might include country and state_or_province columns.
You can partition by country and then partition those further by state_or_province, so customers from Ontario,
Canada would be in the country=ca/state_or_province=on/ partition directory.
This can be extremely helpful for large amounts of data that you want to access either by country or by state or province.
However, using multiple columns increases the danger of creating too many partitions, so you must take extra care when doing so.
The risk of creating too many partitions is why Hive includes the property
hive.exec.dynamic.partition.mode, set to strict by default, which must be reset to nonstrict before you can create a partition.
Rather than automatically and mechanically resetting that property when you're about to load data dynamically,
take it as an opportunity to think about the partitioning columns
and maybe check the number of unique values you would get when you load the data.
And that's all.
I have created one bucketed table on timeslot column which has value from 0 to 23 and datatype of timeslot column is int
I have created 24 buckets and load 10000000 rows (6GB of data) in the bucketed table
At the same time i created a normal non-bucketed table using same dataset
later I queried on bucketed table as well as non-bucketed table like as below
select * from bucketed_table where timeslot = 15;
select * from non-bucketed_table where timeslot = 15;
both the queries are taking almost same time
I was assuming bucketed table perform far better than non-bucketed table
can anyone let me know if i am doing something wrong or my assumption is completely wrong?
As per my understanding bucketed table only performed better in case of joining with other bucketed table. if we just query on bucketed column there will not be any performance gain as in this case both bucketed table and non-bucketed table scan whole table (data files) and that is why in both the cases same number of mapper are launched
I have created a hive table with gender as bucket column.
create table userinfoBucketed(userid INT,age INT,gender STRING,occupation STRING,zipcode STRING) CLUSTERED BY(gender) INTO 2 BUCKETS ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' STORED AS TEXTFILE;
loading following data from text file into table
(user id | age | gender | occupation | zip code) :
1|24|M|technician|85711
2|53|F|other|94043
3|23|M|writer|32067
4|24|M|technician|43537
5|33|F|other|15213
6|42|M|executive|98101
7|57|M|administrator|91344
8|36|M|administrator|05201
9|29|M|student|01002
10|53|M|lawyer|90703
I have set the hive.enforce.bucketing property to true;
set hive.enforce.bucketing=true;
1, when inserted data into table using load command, buckets are not created. all the data stored in one bucket
load data local inpath '/home/mainnode/u.user' into table userinfobucketed;
Question1, Why the data is not split into 2 buckets?
2, when inserted data into table from other table, data stored in 2 buckets. here is the command I have executed:
insert into table userinfobucketed select * from userinfo where gender='M';
Now bucket1(000000_0) has below data:
1|24|M|technician|85711
4|24|M|technician|43537
6|42|M|executive|98101
7|57|M|administrator|91344
bucket2(000001_0) has below data:
3|23|M|writer|32067
Question2, I do not understand why data got stored into 2 buckets even though all same records has the same gender.
Then I again inserted data into the table using the below command.
insert into table userinfobucketed select * from userinfo where gender='F';
Now 2 more extra buckets (000000_0_copy_1,000001_0_copy_1) are created and data stored into those instead of inserting data into existing buckets. Now that makes total buckets to 4 even though create table is configured into 2 buckets.
Question3 ; Why the extra buckets got created into of copying into existing buckets
please clarify
Thanks
Sean
Q1: Why doesn't this work to insert into a bucketed table?
load data local inpath '/home/mainnode/u.user' into table userinfobucketed;
A1: Take a look at this tutorial for inserting into bucketed tables. Hive does not support loading to bucketed tables directly from a flat file using LOAD DATA INPATH, so you have to LOAD the data into a regular table first then INSERT OVERWRITE into your bucketed table.
Q2: Why was the inserted data split into 2 buckets even though all records had the same value for the bucket column?
A2: Hmm. This is abnormal behavior. You should never see records with the same bucket column value getting hashed into different buckets. I suspect you did not drop the table and recreate it after trying the LOAD DATA INPATH method above in Q1. If that were the case, new buckets would be created on the insert, disregarding what's in the existing buckets, which leads us to the next question...
Q3: Why were extra buckets created instead of inserting into existing buckets?
A3: Hive does not append new data to files on insert. Even though you told Hive that your table is bucketed, it only hashes the data you are currently inserting; it does not take into account the data already in the table.
To maintain the number of buckets set in the table definition, you will have to hash all the data together every time you do an insert, and use INSERT OVERWRITE instead of INSERT INTO to overwrite the table.
Generally this is much easier to do if your table is partitioned, so you're not copying and re-hashing your whole table every time you have to do an insert. And speaking of partitioning, since it is such low cardinality, gender is much better suited as a partition value than a bucket value. This article does a pretty good job at explaining this concept.
Bucketing is driven by the hash of the column. Apparently M and F are resulting in the same hash. You might consider making the gender part of the partitioning key - to ensure they end up in different physical files.