How to fetch the hive table partition min and max value in pyspark/beeline?
show partitions table shows all the partitions.
There is another post on same question but it uses a bash approach. Do we have any solution on pyspark?
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I wonder if there is a limit for partition table by list where each subpartition table contains only one element.
For example, I have this partition table:
CREATE TABLE whatever (
city_id int not null,
country_id int not null,
) PARTITION BY LIST (country_id);
And I create millions of subpartition tables:
CREATE TABLE whatever_1 PARTITION OF whatever
FOR VALUES IN (1);
CREATE TABLE whatever_2 PARTITION OF whatever
FOR VALUES IN (2);
# until millions...
CREATE TABLE whatever_10000000 PARTITION OF whatever
FOR VALUES IN (10000000);
Assuming an index on country_id, would that still work?
Or Will I hit the 65000 limit as described here?
Even with PostgreSQL v13, anything that goes beyond at most a few thousand partitions won't work well, and it's better to stay lower.
The reason is that when you use a partitioned table in an SQL statement, the optimizer has to consider all partitions separately. It has to figure out which of the partitions it has to use and which not, and for all partitions that it uses it has to come up with an execution plan. Consequently, planning time will go up as the number of partitions increases. This may not matter for large analytical queries, where execution time dominates, but it will considerably slow down the execution of small statements.
Use longer lists or use range partitioning.
I have one application to store and query the time series data from multiple sensors. The sensor readings of multiple months needed to be stored. And we also need add more and more sensors in the future. So I need consider the scalability in two dimensions, time and sensor ID. We are using postgresql db for data storage. In addition, to simplify the data query layer design, we want to use one table name to query all the data.
In order to improve the query efficiency and scalability, I am considering to use Partitions for this use case. And I want to create the partitions based on two columns. RANGE for the event time for the readings. And VALUE for the sensor ID. So under the paritioned table, I want to get some sub table as sensor_readings_1week_Oct_2020_ID1, sensor_readings_2week_Oct_2020_ID1, sensor_readings_1week_Oct_2020_ID2. I knew PostgreSQL supports multiple columns Partition, but from most examples I can only see RANGE are used for all the columns. One example is as below. How can I create the multiple column partitions, one is for the time RANGE, another is based on the specific sensor ID? Thanks!
CREATE TABLE p1 PARTITION OF tbl_range
FOR VALUES FROM (1, 110, 50) TO (20, 200, 200);
Or are there some better solutions besides Paritions for this use case?
The two level partitions is a good solution for my use case. It improves the efficiency a lot.
CREATE TABLE sensor_readings (
id bigserial NOT NULL,
create_date_time timestamp NULL DEFAULT now(),
value int8 NULL
) PARTITION BY LIST (id);
CREATE TABLE sensor_readings_id1
PARTITION OF sensor_readings
FOR VALUES IN (111) PARTITION BY RANGE(create_date_time);
CREATE TABLE sensor_readings_id1_wk1
PARTITION OF sensor_readings_id1
FOR VALUES FROM (''2020-10-01 00:00:00'') TO (''2020-10-20 00:00:00'');
I have a table partitioned by range in Postgres 10.6. Is there a way to tell one of its partitions to accept NULL for the column used as partition key?
The reason I need this is: my table size is 200GB and it's actually not yet partitioned. I want to partition it going forward, so I thought I would create an initial partition including all of the current rows, and then at the start of each month I would create another partition for that month's data.
The issue is, currently this table doesn't have the column I'll use for partitioning, so I want to add the column (initially null) and then tell that initial partition to hold all rows that have null in the partitioning key.
Another option would be to not add the column as null but to set an initial date value, but that would be time and space consuming because of the size of that table.
I would upgrade to v11 and initially define the partitioned table with just a default partition that contains all the NULL values.
Then you can add other partitions and gradually move the data by updating the NULL values.
I'm new to table partitions in Postgres and have one doubt.
Let us assume I have a table:
product_visitors
I can create multiple partitions like:
product_visitors_year_2017
product_visitors_year_2018
etc.
I can create a trigger which can redirect insertion on product_visitors to appropriate table.
My question is, what if I want to aggregate on full data of product_visitors? For example, products and their visit count
As I understand, at the moment, data resides in year wise tables instead of main table
In Postgres 10 inserts will automatically be routed to the correct partition.
If you select from the "base table" product_visitors without any condition limiting the rows to one (or more) specific partitions, Postgres will automatically read the data from all partitions.
So
select count(*)
from product_visitors;
will count the rows in all partitions.
I want to partition an external table in hive based on range of numbers. Say numbers with 1 to 100 go to one partition. Is it possible to do this in hive?
I am assuming here that you have a table with some records from which you want to load data to an external table which is partitioned by some field say RANGEOFNUMS.
Now, suppose we have a table called testtable with columns name and value. The contents are like
India,1
India,2
India,3
India,3
India,4
India,10
India,11
India,12
India,13
India,14
Now, suppose we have a external table called testext with some columns along with a partition column say, RANGEOFNUMS.
Now you can do one thing,
insert into table testext partition(rangeofnums="your value")
select * from testtable where value>=1 and value<=5;
This way all records from the testtable having value 1 to 5 will come into one partition of the external table.
The scenario is my assumption only. Please comment if this is not the scenario you have.
Achyut