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'm new to Hive and facing some problem. I'm learning bucketing right now and my task is to create a Hive table that consists of 2 buckets, then put at least 5 records into that table. Well, that part is clear I think:
CREATE TABLE <tablename>(id INT,field2 STRING,field3 TINYINT) CLUSTERED BY(id) INTO 2 BUCKETS;
For populating the table I simply used insert into values(...) statement. What I don't really know is the following - I have to run this query:
SELECT * FROM <tablename> TABLESAMPLE(BUCKET 1 OUT OF 2 ON id)
When I run it it returns 0 rows and I don't know why. I tried to look it up on the internet but didn't find exact answer. If I replace the id with an other field in the table it returns the rows in that bucket. So can someone explain it please?
Here I give you some tips for create and insert in bucketing tables.
Bucketing is an approach for improving Hive query performance.
Bucketing stores data in separate files, not separate subdirectories like partitioning.
It divides the data in an effectively random way, not in a predictable way like partitioning.
When records are inserted into a bucketed table, Hive computes hash codes of the values in the specified bucketing column and uses these hash codes to divide the records into buckets.
For this reason, bucketing is sometimes called hash partitioning.
The goal of bucketing is to distribute records evenly across a predefined number of buckets.
Bucketing can improve the performance of joins if all the joined tables are bucketed on the join key column.
For more on bucketing, see the page of the Hive Language Manual describing bucketed tables, at BucketedTables
As an example of bucketing:
Let us see how we can create Bucketed Tables in Hive.
Bucketed tables is nothing but Hash Partitioning in conventional databases.
We need to specify the CLUSTERED BY Clause as well as INTO BUCKETS to create Bucketed table.
CREATE TABLE orders_buck (
order_id INT,
order_date STRING,
order_customer_id INT,
order_status STRING
) CLUSTERED BY (order_id) INTO 8 BUCKETS
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';
DESCRIBE FORMATTED orders_buck;
Let us see how we can add data to bucketed tables.
Typically we use INSERT command to get data into bucketed tables, as source data might not match the criterial of our bucketed table.
If the data is in files, first we need to get data to stage and then insert into bucketed table.
We already have data in orders table, let us use to insert data into our bucketed table orders_buck
hive.enforce.bucketing should be set to true.
Here is the example of inserting data into bucketed table from regular managed or external table.
SET hive.enforce.bucketing;
SET hive.enforce.bucketing=true;
INSERT INTO orders_buck
SELECT * FROM orders;
-- check out into the directory of the bucketed table if the
-- number of files is equal to number of buckets
dfs -ls /user/hive/warehouse/training_retail.db/orders_buck;
SELECT * FROM orders_buck TABLESAMPLE(BUCKET 1 OUT OF 2 ON order_id);
-- In my case this query works perfectly well
We are evaluating cloud warehouse options to build a analytics solution. We need to provide trend analysis per day, per customer across many customers (400+), ratio of queries for across these two dimensions are equal. My initial thought is to create Date partitioned table one per customer, so for queries per customer I limit the scan to one particular day and for queries across all customer I use table wildcard feature.
Questions:
Is there a way to partition by Date and customer, so I can store all data in one table and still limit data scan volume?
If ans to #1 is no, What is the performance impact of querying across 400 tables Vs one table (same amount of data)
Hash partitioning and partitioning by specific fields in a table are not supported yet, so this is not feasible now.
If you query the 400 tables using wildcards and filter customers using _TABLESUFFIX, the query will only read the matching tables and you'll only be charged for those tables; if you query one table then you can filter dates using _PARTITIONTIME and you'll only be charged for the matching partitions. Performance wise less metadata is read if you query one table, but that shouldn't be much for 400 tables.
im planning to build a new ads system and we are considering to use google bigquery.
ill quickly describe my data flow :
Each User will be able to create multiple ADS. (1 user, N ads)
i would like to store the ADS impressions and i thought of 2 options.
1- create a table for impressions , for example table name is :Impressions fields : (userid,adsid,datetime,meta data fields...)
in this options of all my impressions will be stored in a single table.
main pros : ill be able to big data queries quite easily.
main cons: table will be hugh, and with multiple queries, ill end up paying too much (:
option 2 is to create table per ads
for example, ads id 1 will create
Impression_1 with fields (datetime,meta data fields)
pros: query are cheaper, data table is smaller
cons: todo big dataquery sometimes ill have to create a union and things will complex
i wonder what are your thoughts regarding this ?
In BigQuery it's easy to do this, because you can create tables per each day, and you have the possibility to query only those tables.
And you have Table wildcard functions, which are a cost-effective way to query data from a specific set of tables. When you use a table wildcard function, BigQuery only accesses and charges you for tables that match the wildcard. Table wildcard functions are specified in the query's FROM clause.
Assuming you have some tables like:
mydata.people20140325
mydata.people20140326
mydata.people20140327
You can query like:
SELECT
name
FROM
(TABLE_DATE_RANGE(mydata.people,
TIMESTAMP('2014-03-25'),
TIMESTAMP('2014-03-27')))
WHERE
age >= 35
Also there are Table Decorators:
Table decorators support relative and absolute <time> values. Relative values are indicated by a negative number, and absolute values are indicated by a positive number.
To get a snapshot of the table at one hour ago:
SELECT COUNT(*) FROM [data-sensing-lab:gartner.seattle#-3600000]
There is also TABLE_QUERY, which you can use for more complex queries.