I have a hive table which has plenty of partitions, I want to get only 100 partitions when I execute show partitions table name command .
Well, It's not possible to give clause with SHOW PARTITIONS query.
SHOW PARTITIONS lists all the existing partitions for a given base table. And those partitions are listed in alphabetical order.
But if you want to get the limited partitions, you can filter it based on the partitions like :
SHOW PARTITIONS table_name PARTITION(ds='2010-03-03');
These kind of queries will give you limited resultset.
For more information, you can refer the documentation.
Related
I am currently working on the Optimization of a huge table in Google's BigQuery. The tables has approximately 19 billions records resulting in a total size of 5.2 TB. In order to experiment on performance with regards to clustering and time partitioning, I duplicated the table with a Time Partitioning on a custom DATE MyDate column which is frequently used in queries.
When performing a query with a WHERE clause (for instance, WHERE(MyDate) = "2022-08-08") on the time partitioned table, the query is quicker and only reads around 20 GB compared to the 5.2 TB consumed by the table without partition. So far, so good.
My issue, however, arises when applying an aggregated function, i.e. in my case a MAX(MyDate): the query on the partitioned and the non-partitioned tables read the same amount of data and execute in roughly the same time. However, I would have expected the query on the partitioned table to be way quicker as it only needs to scan a single partition.
There seem to be workarounds by fetching the dataset's metadata (information schema) as described here. However, I would like to avoid solutions like this as it adds complexity to our queries.
Are there a more elegant ways to get the MAX of a time-partitioned BigQuery table based on a custom column without scanning the whole table or fetching metadata from the information schema?
When specifying TIMESTAMP column as partition - The data is saved on the disk by the partition allows each access.
Now, BigQuery allows to also define up to 4 columns which will used as cluster field.
If I get it correctly the partition is like PK and the cluster fields are like indexes.
So this means that the cluster fields has nothing to do with how records are saved on the disk?
If I get it correctly the partition is like PK
This is not correct, Partition is not used to identify a row in the table rather enable BigQuery to Store each partitioned data in a different segment so when you scan a table by Partition you ONLY scan the specified partitions and thus reduce your scanning cost
cluster fields are like indexes
This is correct cluster fields are used as pointers to records in the table and enable quick/minimal cost access to data regardless to the partition. This means Using cluster fields you can query a table cross partition with minimal cost
I like #Felipe image from his medium post which gives nice visualization on how data is stored.
Note: Partitioning happens on the time of the insert while clustering happens as a background job performed by BigQuery
I have large size table , close to 1 GB and the size of this table is growing every week, it has total rows as 190 millions, I started getting alerts from HANA to partition this table, so I planned to partition this with column which is frequently used in Where clause.
My HANA System is scale out system with 8 nodes.
In order to compare the partition query performance difference with this un-partitioned table, I created calculation views on top of this un-partitioned table and recorded the query performance.
I partitioned this table using HASH method and by number of servers, and recorded the query performance. By this way I would have good data distribution across servers. I created calculation view and recorded query performance.
To my surprise I have found that my un-partitioned table calculation view query is performing better in comparison to partitioned table calculation view.
This was really shock. Not sure why non partitioned table Calculation view responding better to partitioned table Calculation view.
I have plan viz output files but not sure where to attach it.
Let me know why this is the behaviour?
Ok, this is not a straight-forward question that can be answered correctly as such.
What I can do though is to list some factors that likely will play a role here:
a non-partitioned table needs a single access to the table structure while the partitioned version requires at least one access for each partition
if the SELECT is not actually providing a WHERE condition that can be evaluated by the HASH function used for the partitioning, then all partitions always have to be evaluated and no partition pruning can take place.
HASH partitioning does not take any additional knowledge about the data into account, which means that similar data does not get stored together. This has a negative impact on data compression. Also, each partition requires its own set of value dictionaries for the columns where a single-partition/non-partitioned table only needs one dictionary per column.
You mentioned that you are using a scale-out system. If the table partitions are distributed across the different nodes, then every query will result in cross-node network communication. That is an additional workload and waiting time that simply does not exist with non-partitioned tables.
When joining partitioned tables each partition of the first table has to be joined with each partition of the second table, if no partition-wise join is possible.
There are other/more potential reasons for why a query against partitioned tables can be slower than against a non-partitioned table. All this is extensively explained in the SAP HANA Administration Guide.
As a general guidance, tables should only be partitioned if that cannot be avoided and when the access pattern of queries are well understood. It is definitively not a feature that you just "switch on" and everything will just work fine.
How indexes in hive are different than partitions? both improves query performance as per my knowledge then in what way they differ?
What are the situations I'll be using indexing or partitioning?
Can i use them together?
Kindly suggest
Partitions allow users to store data files stored in different HDFS directories (based on chosen parameter, date for example, if you want to store your datafiles by date) thus, minimizing the number of files to scan when users run queries.
While indexes help in fetching data faster, indexes require index tables to built where the data to be indexed is stored. This leads to storing the data twice.
partition:
Think about that you have a table keeping transactions created from your applications. this table get bigger day by day,
if you partition this table based on day interval ,database creates like table at each day interval but you see only one table. It makes your dailiy basis query more effective.
Index.
Index is used to access your table records fastly.
I'm learning table partitioning.
When I read this page, it said that
The TransactionHistoryArchive table must have the same design schema as the TransactionHistory table. There must also be an empty partition to receive the new data. In this case, TransactionHistoryArchive is a partitioned table that consists of just two partitions.
And with the following picture, we can see that TransactionHistory has 12 partitions, but TransactionHistoryArchive just has 2 partitions.
Illustration http://i.msdn.microsoft.com/dynimg/IC38652.gif
How could it possible? Please help me to understand it.
As long as two individual partitions have identical schema and the same boundary values you can switch them. They don't need to have the same partition scheme or function.
This is because SQL Server ensures that the binary data of those partitions on disk is compatible. That's the magic of partitioning and why you can move arbitrary amounts of data as a quick metadata-only operation.