I want to create a sample database using composite partition. I know about Range Partition and List Partition. But, I don't have enough knowledge about Hash Values and how to create Hash Partition in my database?. So, I have decided that I should make a sample database using Composite Partition and I want to use Range Partition and Hash Partition in it. Can anybody describe it more and in easy word so, i can understand well about Hash Partition as well as Composite Partition.
I have also read some documents on internet. But, I could not understand how to create Hash Partition and How to create Composite Partition in my database. Actually I don't have enough knowledge about Hash Value and Hash Functoin. I have read about it but, I could not understand very well. I need a simple definition.
Definition of Horizontal Partition & Vertical Partition
Partition (database)
Hash Functions
Composite Partitioning feature is not available in SQL Server 2008. Only Range Partitioning is available in SQL Server.
Although the partitioning column must be a single column, it does not need to be numeric and it can be calculated so that the range can include multiple columns.
For instance it is common to partition on datetime data by month. This will work well, because that data is usually in a single column, but what do you do if you have data for multiple companies and you also want to partition by company? For this you could use a computed column for the partitioning column. This will create a computed column using the ‘company id’ and ‘order month’ which is then used for the partitions. It will partition three companies for the first three months of 2007.
the computed column must be persisted to form the partitioning column.
CREATE PARTITION FUNCTION MyPartitionRange (INT) AS RANGE LEFT FOR VALUES (1200701,1200702,1200703,2200701,2200702,2200703,3200701,3200702,3200703)
CREATE PARTITION SCHEME MyPartitionScheme AS PARTITION MyPartitionRange ALL TO ([PRIMARY])
CREATE TABLE CompanyOrders
( Company_id INT ,
OrderDate datetime ,
Item_id INT ,
Quantity INT ,
OrderValue decimal(19,5) ,
PartCol AS Company_id * 10000 + CONVERT(VARCHAR(4),OrderDate,112) persisted
) ON MyPartitionScheme (PartCol)
Related
We're using SQL Server 2019. Our fact tables utilize datetime2 but I want to partition on year.
I don't have sysadmin privs so I can't set up different filegroups. I can create partition functions and partition schemes, but it isn't clear to me how to set up the partition scheme so that when I partition the table on ActivityLog for example that it will store entries in their respective year partition.
I've searched the web and haven't found answers as to how it all works.
Partitioning by year on a datetime2 column in a fact table can be a useful technique for managing large data sets, improving query performance, and reducing maintenance costs. Here are the steps to set up partitioning by year:
Define a partition function: A partition function defines the ranges or
boundaries for partitioning the data. In this case, you would define a
partition function that partitions the data by year. For example, the
following code creates a partition function that partitions the data by
year:
CREATE PARTITION FUNCTION pfFactTableByYear (datetime2(0))
AS RANGE RIGHT FOR VALUES
('2010-01-01T00:00:00', '2011-01-01T00:00:00', '2012-01-01T00:00:00', '2013-01-01T00:00:00', '2014-01-01T00:00:00', '2015-01-01T00:00:00', '2016-01-01T00:00:00', '2017-01-01T00:00:00', '2018-01-01T00:00:00', '2019-01-01T00:00:00', '2020-01-01T00:00:00')
Define a partition scheme: A partition scheme maps the partition function to
a set of filegroups. In this case, you would define a partition scheme that
maps the partition function to a set of filegroups. For example, the
following code creates a partition scheme that maps the partition function
to a set of filegroups:
CREATE PARTITION SCHEME psFactTableByYear
AS PARTITION pfFactTableByYear
TO (fg2010, fg2011, fg2012, fg2013, fg2014, fg2015, fg2016, fg2017, fg2018, fg2019, fg2020)
Create the fact table with partitioning: You would create the fact table
with the partition scheme defined in step 2. For example, the following code
creates a fact table with partitioning by year:
CREATE TABLE FactTable
(
Id INT IDENTITY(1,1),
DateColumn datetime2(0) NOT NULL,
ValueColumn decimal(18,2) NOT NULL,
CONSTRAINT PK_FactTable PRIMARY KEY (Id, DateColumn)
)
ON psFactTableByYear(DateColumn)
This creates a fact table with a primary key that includes the partitioning column (DateColumn), and maps the partition scheme to the fact table's data filegroups.
Load data into the fact table: Once the fact table is created, you can load
data into it using standard INSERT statements.
Perform maintenance tasks: As time goes on, new partitions will need to be
created to accommodate new data. You can automate this process using
partition switching or by running a maintenance script that creates new
partitions on a regular basis. You may also want to periodically archive or
remove old data to keep the data set manageable.
Note that partitioning by year is just one option for partitioning a fact table, and the partition function and scheme would need to be adjusted accordingly for other partitioning strategies, such as partitioning by month, quarter, or some other time period.
I have a table in snowflake with around 1000 columns, i have an id column which is of integer type
when i run query like
select * from table where id=12
it is scanning all the micro-paritions .I am expecting that snowflake will maintain metadata of min/max of id column and based on that it should scan only one partition rather than all the partition.
In this doc https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html its mentioned that they maintain min/max , disticnt value of columns in each micro-partition.
How can i take advantage of partititon pruning in this scenario?Currently even for unique id snowflake is scanning all the partitions.
It's a little more complicated than that unfortunately. Snowflake would only scan a single partition if your table was perfectly clustered by your id column, which it probably isn't, nor should it be. Snowflake is a data warehouse and isn't ideal for single-row lookups.
You could always cluster your table by your id column but you usually don't want to do this in a data warehouse. I would recommend reading this document to understand how table clustering works.
I am working on a heavy record set database in MS SQL 2016. So I want to use row table partition feature to improve speed.
As we know partition feature is working on partition column of a table. Let's say [Date Column] of a table. In our scenario, have many tables that need to partition because of heaver record set in 5 to 7 tables. Each table not have that [Date column]. Also not possible to add that column in each table.
So is there any way I can select partition column of another table or something else.
The best option is to add a common column to all tables that you will then use to partition by.
You must already have a way of relating the different tables to each other so you can use this to tag each table with the correct Partition column.
This column could be as simple as an int with YYYYMM as values for monthly partitions.
You also need to make sure your queries are "Partition Aware".
This means that you should include this column in your WHERE Clause and also your JOIN Clauses for any queries.
Use Query Plans to make sure you are getting Partition Elimination on your queries.
If you can't change the model (but can add partitions???) then you could implement the partitioning with different columns in each table provided you have a single column in each table that you can partition on named ranges - but if you have 1-many relationships then it is unlikely that the child tables keys will be consecutive relative to the parent table. Note that this approach will make your "partition aware" queries more complex to craft.
Currently, I am dealing with Cassandra.
While reading a blog post, it is said:
When issuing a CQL query, you must include all partition key columns,
at a minimum.
(https://shermandigital.com/blog/designing-a-cassandra-data-model/)
However, in my database it seems like it possible without including all partition keys. Here the table:
CREATE TABLE usertable (
personid text,
name text,
"timestamp" timestamp,
active boolean,
PRIMARY KEY ((personid, name), timestamp)
) WITH
CLUSTERING ORDER BY ("timestamp" DESC)
AND comment=''
AND read_repair_chance=0
AND dclocal_read_repair_chance=0.1
AND gc_grace_seconds=864000
AND bloom_filter_fp_chance=0.01
AND compaction={ 'class':'org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy',
'max_threshold':'32',
'min_threshold':'4' }
AND compression={ 'chunk_length_in_kb':'64',
'class':'org.apache.cassandra.io.compress.LZ4Compressor' }
AND caching={ 'keys':'ALL',
'rows_per_partition':'NONE' }
AND default_time_to_live=0
AND id='23ff16b0-c400-11e8-55c7-2b453518a213'
AND min_index_interval=128
AND max_index_interval=2048
AND memtable_flush_period_in_ms=0
AND speculative_retry='99PERCENTILE';
So I can do select * from usertable where personid = 'ABC-02';. However, according to the blog post, I have to include timestamp as well.
Can someone explain this?
In cassandra, partition key spreads data around cluster. It computes the hash of partition key and determine the location of data in the cluster.
One exception is, if you use ALLOW FILTERING or secondary index it does not require you too include all partition keys in where query.
For further information take a look at blog post:
The purpose of a partition key is to split the data into partitions
where an entire partition is stored on a single node in the cluster
(with each node storing many partitions). When data is read or written
from the cluster, a function called Partitioner is used to compute the
hash value of the partition key. This hash value is used to determine
the node/partition which contains that row. The clustering key is used
further to search for a row within a given partition.
Select queries in Apache Cassandra look a lot like select queries from
a relational database. However, they are significantly more
restricted. The attributes allowed in ‘where’ clause of Cassandra
query must include the full partition key and additional clauses may
only reference the clustering key columns or a secondary index of the
table being queried.
Requiring the partition key attributes in the ‘where’ helps Cassandra
to maintain constant result-set retrieval time as the cluster is
scaled-out by allowing Cassandra to determine the partition, and thus
the node (and even data files on disk), that the query must be
directed to.
If a query does not specify the values for all the columns from the
primary key in the ‘where’ clause, Cassandra will not execute it and
give the following warning :
‘InvalidRequest: Error from server: code=2200 [Invalid query]
message=”Cannot execute this query as it might involve data filtering
and thus may have unpredictable performance. If you want to execute
this query despite the performance unpredictability, use ALLOW
FILTERING” ‘
https://www.instaclustr.com/apache-cassandra-scalability-allow-filtering-partition-keys/
https://www.datastax.com/dev/blog/a-deep-look-to-the-cql-where-clause
According to your schema, your timestamp column is the clustering column, the sorting column, no part of the partition key. That’s why it is not required.
(personid, name) are your partitions columns.
Using SQL Server 2005 and 2008.
I've got a potentially very large table (potentially hundreds of millions of rows) consisting of the following columns:
CREATE TABLE (
date SMALLDATETIME,
id BIGINT,
value FLOAT
)
which is being partitioned on column date in daily partitions. The question then is should the primary key be on date, id or value, id?
I can imagine that SQL Server is smart enough to know that it's already partitioning on date and therefore, if I'm always querying for whole chunks of days, then I can have it second in the primary key. Or I can imagine that SQL Server will need that column to be first in the primary key to get the benefit of partitioning.
Can anyone lend some insight into which way the table should be keyed?
As is the standard practice, the Primary Key should be the candidate key that uniquely identifies a given row.
What you wish to do, is known as Aligned Partitioning, which will ensure that the primary key is also split by your partitioning key and stored with the appropriate table data. This is the default behaviour in SQL Server.
For full details, consult the reference Partitioned Tables and Indexes in SQL Server 2005
There is no specific need for the partition key to be the first field of any index on the partitioned table, as long as it appears within the index it can then be aligned to the partition scheme.
With that in mind, you should apply the normal rules for index field order supporting the most queries / selectivity of the values.