Drop non-partitioned table. Rename the duplicate table to original table name. Partition exchange. Create duplicate table of non-partitioned table with required partitions. Suppose we have a non-partitioned order table with order as name. These are the steps to create a partitioned table. Partition methods in Detail with Examples. Previous Article Next Article.
Older Post Newer Post Home. However, you should note that partition names are not case-sensitive. When you specify the number of partitions for the table, this must be expressed as a positive, nonzero integer literal with no leading zeros, and may not be an expression such as 0.
Decimal fractions are not permitted. Exchanging Partitions and Subpartitions with Tables. Maintenance of Partitions. Obtaining Information About Partitions. Restrictions and Limitations on Partitioning. Partitioning Limitations Relating to Storage Engines. Partitioning Limitations Relating to Functions. Partitioning and Locking. You do this by either defining a complete partitioned table as being compressed, or by defining it on a per-partition level.
Partitions without a specific declaration inherit the attribute from the table definition or, if nothing is specified on table level, from the tablespace definition. To decide whether or not a partition should be compressed or stay uncompressed adheres to the same rules as a nonpartitioned table. However, due to the capability of range and composite partitioning to separate data logically into distinct partitions, such a partitioned table is an ideal candidate for compressing parts of the data partitions that are mainly read-only.
It is, for example, beneficial in all rolling window operations as a kind of intermediate stage before aging out old data. With data segment compression, you can keep more old data online, minimizing the burden of additional storage consumption. The partitions can contain data or can be empty. The access and maintenance of a partially or fully compressed partitioned table are the same as for a fully uncompressed partitioned table. Everything that applies to fully uncompressed partitioned tables is also valid for partially or fully compressed partitioned tables.
If you want to use table compression on partitioned tables with bitmap indexes, you need to do the following before you introduce the compression attribute for the first time:. The first time you make a compressed partition part of an already existing, fully uncompressed partitioned table, you must either drop all existing bitmap indexes or mark them UNUSABLE prior to adding a compressed partition. This must be done irrespective of whether any partition contains any data. It is also independent of the operation that causes one or more compressed partitions to become part of the table.
This does not apply to a partitioned table having B-tree indexes only. This rebuilding of the bitmap index structures is necessary to accommodate the potentially higher number of rows stored for each data block with table compression enabled and must be done only for the first time.
All subsequent operations, whether they affect compressed or uncompressed partitions, or change the compression attribute, behave identically for uncompressed, partially compressed, or fully compressed partitioned tables. To avoid the recreation of any bitmap index structure, Oracle recommends creating every partitioned table with at least one compressed partition whenever you plan to partially or fully compress the partitioned table in the future.
This compressed partition can stay empty or even can be dropped after the partition table creation. Having a partitioned table with compressed partitions can lead to slightly larger bitmap index structures for the uncompressed partitions.
The bitmap index structures for the compressed partitions, however, are in most cases smaller than the appropriate bitmap index structure before table compression. This highly depends on the achieved compression rates. You have to rebuild them afterward, as follows:. The following statement merges two existing partitions into a new, compressed partition, residing in a separate tablespace.
The local bitmap indexes have to be rebuilt afterward, as follows:. Partition pruning is an essential performance feature for data warehouses. This enables Oracle Database to perform operations only on those partitions that are relevant to the SQL statement.
Oracle Database prunes partitions when you use range, LIKE , equality, and IN -list predicates on the range or list partitioning columns, and when you use equality and IN -list predicates on the hash partitioning columns. Partition pruning dramatically reduces the amount of data retrieved from disk and shortens processing time, thus improving query performance and resource utilization.
If you partition the index and table on different columns with a global partitioned index , partition pruning also eliminates index partitions even when the partitions of the underlying table cannot be eliminated.
Static pruning occurs at compile-time, with the information about the partitions accessed beforehand while dynamic pruning occurs at run-time, meaning that the exact partitions to be accessed by a statement are not known beforehand. Partition pruning affects the statistics of the objects where pruning will occur and will therefore also affect the execution plan of a statement.
On composite partitioned objects, Oracle can prune at both the range partition level and at the hash or list subpartition level using the relevant predicates. When using range partitioning, Oracle accesses only partitions sal99q2 and sal99q3 , representing the partitions for the third and fourth quarters of The mapping between the subpartition and the predicate is calculated based on Oracle's internal hash distribution function.
For a number of cases, Oracle determines the partitions to be accessed at compile time. This happens when the predicates on the partitioning columns use a range predicate.
In addition, the predicates must only use constants so that Oracle can determine the start and stop partition numbers at compile time. When this happens, the actual partition numbers show up in the partition start partition stop columns of the explain plan. The following is an example:. Oracle Database converts the pruning predicates into a partition number at compile time whenever it can. However, there are a number of cases when this is not possible.
For example, if a predicate on a partitioning column involves a bind variable, Oracle cannot determine the partition numbers at compile time.
Even if bind variables are not used, for certain classes of predicates such as IN -list predicates Oracle accesses a list of predicates not a range. In all these cases, the explain plan will show KEY in the partition start and stop columns. There is also some information about the type of predicate used.
For example, if an IN -list predicate was used, the plan output will show KEY I in the partition start and stop column. For example, consider the following:. For parallel plans only, the partition start and stop columns contain the partition pruning information; the operation column will contain information for the parallel operation, as shown in the following example:. The optimizer uses a wide variety of predicates for pruning.
The three predicate types, equality, range, and IN -list are the most commonly used cases of partition pruning. As an example, consider the following query:. Because there is an equality predicate on the partitioning column of sales , this query will prune down to a single predicate and this will be reflected in the explain plan, as shown in the following:.
The partitioning type plays a role in which predicates can be used. Range predicates cannot be used for pruning on hash partitioned tables while they can be used for all other partitioning strategies. However, on list partitioned tables, range predicates do not map to a range predicates do not map to a range of partitions. Equality and IN -list predicates can be used to prune with all the partitioning methods. Oracle also prunes in the presence of more complex predicates or SQL statements involving partitioned tables.
A common situation is when a partitioned table is joined to the subset of another table, limited by a WHERE condition.
For example, consider the following query:. If we performed a nested loop join with times on the right hand side, then we would only access the partition corresponding to this row from the times table. But, if we performed a hash or sort merge join, this would not be possible.
If the table with the WHERE predicate is relatively small compared to the partitioned table, and the expected reduction of records or partitions for the partitioned table is significant, Oracle will perform dynamic partition pruning using a recursive subquery.
The decision whether or not to invoke subquery pruning is an internal cost-based decision of the optimizer. Another example using advanced pruning is the following, which uses an OR predicate:. This query joins the sales and costs tables in the sh sample schema. This OR predicate is used to prune the partitions in sales table and a single join between sales and costs table is performed.
The plan is as follows:. Oracle also does additional pruning when a column is range partitioned on multiple columns. As long as Oracle can guarantee that a particular predicate cannot be satisfied in a particular partition, the partition will be skipped. This allows Oracle to optimize cases where there are range predicates on more than one column or in the case where there are no predicates on a prefix of the partitioning columns.
An Oracle DATE datatype is not a character string, but is only represented as such when querying the database; the format of the representation is defined by the NLS setting of the instance or the session. Consequently, the same reverse conversion has to happen when inserting data into a DATE filed or specifying a predicate on such a field. Rewriting the example of the "Basic Partition Pruning Techniques" slightly, using the two-digit date format RR causes the predicate to become non-deterministic, thus causing dynamic pruning:.
The keyword KEY for both columns means that partition pruning occurs at run-time. There are several cases when the optimizer cannot perform any pruning. One of the most common reasons is when an operator is on top of a partitioning column.
This could be an explicit operator for example, a function or even an implicit operator introduced by Oracle as part of the necessary data type conversion for executing the statement.
Partition-wise joins reduce query response time by minimizing the amount of data exchanged among parallel execution servers when joins execute in parallel. This significantly reduces response time and improves the use of both CPU and memory resources. In Oracle Real Application Clusters RAC environments, partition-wise joins also avoid or at least limit the data traffic over the interconnect, which is the key to achieving good scalability for massive join operations.
A full partition-wise join divides a large join into smaller joins between a pair of partitions from the two joined tables. To use this feature, you must equipartition both tables on their join keys. For example, consider a large join between a sales table and a customer table on the column customerid. The query "find the records of all customers who bought more than articles in Quarter 3 of " is a typical example of a SQL statement performing such a join. The following is an example of this:.
This large join is typical in data warehousing environments. The entire customer table is joined with one quarter of the sales data. In large data warehouse applications, this might mean joining millions of rows. The join method to use in that case is obviously a hash join. You can reduce the processing time for this hash join even more if both tables are equipartitioned on the customerid column.
This enables a full partition-wise join. When you execute a full partition-wise join in parallel, the granule of parallelism is a partition. As a result, the degree of parallelism is limited to the number of partitions. For example, you require at least 16 partitions to set the degree of parallelism of the query to You can use various partitioning methods to equipartition both tables on the column customerid with 16 partitions. These methods are described in these subsections.
Because you are using the same hash function to distribute the same information customer ID into the same number of hash partitions, you can join the equivalent partitions. They are storing the same values. In serial, this join is performed between pairs of matching hash partitions, one at a time. When one partition pair has been joined, the join of another partition pair begins.
The join completes when the 16 partition pairs have been processed. Parallel execution of a full partition-wise join is a straightforward parallelization of the serial execution. Instead of joining one partition pair at a time, 16 partition pairs are joined in parallel by the 16 query servers. Figure illustrates the parallel execution of a full partition-wise join.
In Figure , assume that the degree of parallelism and the number of partitions are the same, in other words, 16 for both.
Defining more partitions than the degree of parallelism may improve load balancing and limit possible skew in the execution. If you have more partitions than query servers, when one query server completes the join of one pair of partitions, it requests that the query coordinator give it another pair to join. This process repeats until all pairs have been processed. This method enables the load to be balanced dynamically when the number of partition pairs is greater than the degree of parallelism, for example, 64 partitions with a degree of parallelism of In Oracle Real Application Clusters environments running on shared-nothing or MPP platforms, placing partitions on nodes is critical to achieving good scalability.
Partition pairs should be spread over all nodes to avoid bottlenecks and to use all CPU resources available on the system. Nodes can host multiple pairs when there are more pairs than nodes. For example, with an 8-node system and 16 partition pairs, each node receives two pairs. This method is a variation of the hash-hash method. The sales table is a typical example of a table storing historical data.
For all the reasons mentioned under the heading When to Use Range Partitioning range is the logical initial partitioning method. Also assume you have two years and that each partition represents a quarter. The customers table can still use hash partitioning with 16 partitions. When you use the method just described, a full partition-wise join works similarly to the one created by the hash-hash method.
The join is still divided into 16 smaller joins between hash partition pairs from both tables. The difference is that now each hash partition in the sales table is composed of a set of 8 subpartitions, one from each range partition. Figure illustrates how the hash partitions are formed in the sales table.
Each cell represents a subpartition. Each row corresponds to one range partition, for a total of 8 range partitions. Each range partition has 16 subpartitions. Each column corresponds to one hash partition for a total of 16 hash partitions; each hash partition has 8 subpartitions. Note that hash partitions can be defined only if all partitions have the same number of subpartitions, in this case, Hash partitions are implicit in a composite table.
Reads can take advantage of time bound and specific host and metric predicates to prune partitions. New range partitions can be added, which results in creating 4 additional tablets as if a new column were added to the diagram.
Hash and range partitioning The previous examples showed how the metrics table could be range partitioned on the time column, or hash partitioned on the host and metric columns.
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