This section describes the partitioning features that significantly enhance data access and improve overall application performance. This is especially true for applications that access tables and indexes with millions of rows and many gigabytes of data.
Partitioned tables and indexes facilitate administrative operations by enabling these operations to work on subsets of data. For example, you can add a new partition, organize an existing partition, or drop a partition and cause less than a second of interruption to a read-only application.
Using the partitioning methods described in this section can help you tune SQL statements to avoid unnecessary index and table scans (using partition pruning).You can also improve the performance of massive join operations when large amounts of data (for example, several million rows) are joined together by using partition-wise joins. Finally, partitioning data greatly improves manageability ofvery large databases and dramatically reduces the time required for administrative tasks such as backup and restore.
Granularity can be easily added or removed to the partitioning scheme by splitting partitions. Thus, if a table's data is skewed to fill some partitions more than others, the ones that contain more data can be split to achieve a more even distribution.Partitioning also allows one to swap partitions with a table. By being able to easily add, remove, or swap a large amount of data quickly, swapping can be used to keep a large amount of data that is being loaded inaccessible until loading is completed,or can be used as a way to stage data between different phases of use. Some examples are current day's transactions or online archives.
Oracle offers four partitioning methods:
Each partitioning method has different advantages and design considerations. Thus, each method is more appropriate fora particular situation.
Range partitioning maps data to partitions based on ranges of partition key values that you establish for each partition.It is the most common type of partitioning and is often used with dates.For example, you might want to partition sales data into monthly partitions.
Range partitioning maps rows to partitions based on ranges of column values. Range partitioning is defined by the partitioning specification for a table or index in PARTITION BY RANGE(column_list)and by the partitioning specifications for each individual partition in VALUES LESS THAN(value_list), where column_ list is an ordered list of columns that determines the partition to which a row or an index entry belongs.These columns are called the partitioning columns. The values in the partitioning columns of a particular row constitute that row's partitioning key.
value_list is an ordered list of values for the columns in the column list.Each value must be either a literal or a TO_DATE or RPAD function with constant arguments.Only the VALUES LESS THAN clause is allowed. This clause specifies a non-inclusive upper bound for the partitions.All partitions, except the first, have an implicit low value specified by the VALUES LESS THAN literal on the previous partition.Any binary values of the partition key equal to or higher than this literal are added to the next higher partition. Highest partition being where MAXVALUE literal is defined. Keyword, MAXVALUE, represents a virtual infinite value that sorts higher than any other value for the data type, including the null value.
The following statement creates a table sales_range that is range partitioned on the sales_date field:
Note:This table was created with the COMPRESS keyword, thus all partitions inherit this attribute.
Hash partitioning maps data to partitions based on a hashing algorithm that Oracle applies to a partitioning key that you identify.The hashing algorithm evenly distributes rows among partitions, giving partitions approximately the same size. Hash partitioning is the ideal method for distributing data evenly across devices. Hash partitioning is a good and easy-to-use alternative to range partitioning when data is not historical and there is no obvious column or column list where logical range partition pruning can be advantageous.
Oracle Database uses a linear hashing algorithm and to prevent data from clustering within specific partitions, you should define the number of partitions by a power of two(for example,2 4,8).The following statement creates a table sales_hash, which is hash partitioned on the salesman_id field:
Note:You cannot define alternate hashing algorithms for partitions.
List partitioning enables you to explicitly control how rows map to partitions. You do this by specifying a list of discrete values for the partitioning column in the description for each partition. This is different from range partitioning, where a range of values is associated with a partition and with hash partitioning, where you have no control of the row-to-partition mapping. The advantage of list partitioning is that you can group and organize unordered and unrelated sets of data in a natural way.The following example creates a list partitioned table grouping states according to their sales regions:
Partition sales_west is furthermore created as a single compressed partition within sales_list.
An additional capability with list partitioning is that you can use a default partition, so that all rows that do not map to any other partition do not generate an error. For example, modifying the previous example, you can create a default partition as follows:
Composite partitioning combines range and hash or list partitioning. Oracle Database first distributes data into partitions according to boundaries established by the partition ranges. Then, for range-hash partitioning, Oracle uses a hashing algorithm to further divide the data into subpartitions within each range partition. For range-list partitioning, Oracle divides the data intosubpartitions within each range partition based on the explicit list you chose.
You can choose whether or not to inherit the partitioning strategy of the underlying tables. You can create both local and global indexes on a table partitioned by range, hash, or composite methods. Local indexes inherit the partitioning attributes of their related tables. For example, if you create a local index on a composite table, Oracle automatically partitions the local index using the composite method.
Performance Issues for Range, List, Hash, and Composite Partitioning
This section describes performance issues for:
When to Use Range Partitioning
Range partitioning is a convenient method for partitioning historical data. The boundaries of range partitions define the ordering of the partitions in the tables or indexes.
Range partitioning organizes data by time intervals on a column of type DATE. Thus, most SQL statements accessing range partitions focus on timeframes. An example of this is a SQL statement similar to "select data from a particular period in time." In such a scenario, if each partition represents data for one month, the query "find data of month 98-DEC" needs to access only the December partition of year 98. This reduces the amount of data scanned to a fraction of the total data available, an optimization method called partition pruning.
Range partitioning is also ideal when you periodically load new data and purge old data. It is easy to add or drop partitions. It is common to keep a rolling window of data,for example keeping the past 36 months' worth of data online. Range partitioning simplifies this process. To add data from a new month, you load it into a separate table, clean it, index it, and then add it to the range-partitioned table using the EXCHANGE PARTITION statement, all while the original table remains online.Once you add the new partition, you can drop the trailing month with the DROP PARTITION statement.The alternative to using the DROP PARTITION statement can be to archive the partition and make it read only, but this works only when your partitions are in separate tablespaces.
In conclusion, consider using range partitioning when:
The following example creates the table salestable for a period of two years, 1999 and 2000, and partitions it by range according to the column s_salesdate to separate the data into eight quarters, each corresponding to a partition.
When to Use Hash PartitioningThe way Oracle Database distributes data in hash partitions does not correspond to a business or a logical view of the data, as it does in range partitioning. Consequently, hash partitioning is not an effective way to manage historical data. However, hash partitions share some performance characteristics with range partitions. For example, partition pruning is limited to equality predicates. You can also use partition-wise joins, parallel index access, and parallel DML.
Note:In hash partitioning, partition pruning uses only equality orIN-list predicates.
If you add or merge a hashed partition, Oracle automatically rearranges the rows to reflect the change in the number of partitions and subpartitions.The hash function that Oracle uses is especially designed to limit the cost of this reorganization. Instead of reshuffling all the rows in the table, Oracles uses an "add partition" logic that splits one and only one of the existing hashed partitions. Conversely, Oracle coalesces a partition by merging two existing hashed partitions.
Although the hash function's use of "add partition" logic dramatically improves the manageability of hash partitioned tables, it means that the hash function can cause a skew if the number of partitions of a hash partitioned table, or the number of subpartitions in each partition of a composite table, is not a power of two. In the worst case, the largest partition can be twice the size of the smallest. So for optimalperformance, create a number of partitions and subpartitions for each partition that is a power of two. For example, 2, 4, 8, 16, 32, 64, 128, and so on.The following example creates four hashed partitions for the table sales_hash using the column s_productid as the partition key:
Specify partition names if you want to choose the names of the partitions. Otherwise, Oracle automatically generates internal names for the partitions. Also, you can use the STORE IN clause to assign hash partitions to tablespaces in a round-robin manner.
When to Use List Partitioning
You should use list partitioning when you want to specifically map rows to partitions based on discrete values. Unlike range and hash partitioning, multi-column partition keys are not supported for list partitioning. If a table is partitioned by list, the partitioning key can only consist of a single column of the table.
When to Use Composite Range-Hash PartitioningComposite range-hash partitioning offers the benefits of both range and hash partitioning. With composite range-hash partitioning, Oracle first partitions by range. Then, within each range, Oracle creates subpartitions and distributes data within them using the same hashing algorithm it uses for hash partitioned tables.
Data placed in composite partitions is logically ordered only by the boundaries that define the range level partitions. The partitioning of data within each partition has no logical organization beyond the identity of the partition to which the subpartitions belong.
Consequently, tables and local indexes partitioned using the composite range-hash method:
Using Composite Range-Hash PartitioningUse the composite range-hash partitioning method for tables and local indexes if:
Most large tables in a data warehouse should use range partitioning. Composite partitioning should be used for very large tables or for data warehouses with a well-defined need for these conditions. When using the composite method, Oracle stores each subpartition on a different segment.Thus, the subpartitions may have properties that differ from the properties of the table or from the partition to which the subpartitions belong.
The following example partitions the table sales_range_hash by range on the column s_saledate to create four partitions that order data by time.Then, within each range partition, the data is further subdivided into 16 subpartitions by hash on the column s_productid:
each hashed subpartition contains sales data for a single quarter ordered by product code. The total number of subpartitions is 4x8 or 32.
In addition to this syntax, you can create subpartitions by using a subpartition template.This offers better ease in naming and control of location for tablespaces and subpartitions.The following statement illustrates this:
In this example,every partition has the same number of subpartitions.A sample mapping for sal99q1 is illustrated in Table. Similar mappings exist for sal99q2 through sal99q4.
Subpartition MappingSubpartition Tablespace
When to Use Composite Range-List PartitioningComposite range-list partitioning offers the benefits of both range and list partitioning. With composite range-list partitioning, Oracle first partitions by range. Then, within each range, Oracle creates subpartitions and distributes data within them to organize sets of data in a natural way as assigned by the list.
Data placed in composite partitions is logically ordered only by the boundaries that define the range level partitions.
Using Composite Range-List PartitioningUse the composite range-list partitioning method for tables and local indexes if:
Most large tables in a data warehouse should use range partitioning. Composite partitioning should be used for very large tables or for data warehouses with a well-defined need for these conditions. When using the composite method, Oracle stores each subpartition on a different segment. Thus, the subpartitions may have properties that differ from the properties of the table or from the partition to which the subpartitions belong.
This statement creates a table quarterly_regional_sales that is range partitioned on the txn_date field and list subpartitioned on state.
You can create subpartitions in a composite partitioned table using a subpartition template. A subpartition template simplifies the specification of subpartitions by not requiring that a subpartition descriptor be specified for every partition in the table. Instead, you describe subpartitions only once in a template, then apply that subpartition template to every partition in the table.The following statementillustrates an example where you can choose the subpartition name and tablespace locations:
Partitioning and Table Compression
You can compress several partitions or a complete partitioned heap-organized table. 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.
You can also change any existing uncompressed table partition later on, add new compressed and uncompressed partitions, or change the compression attribute as part of any partition maintenance operation that requires data movement, such as MERGE PARTITION, SPLIT PARTITION, or MOVE PARTITION. 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.
Table Compression and Bitmap Indexes
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.
Note:Oracle Database will raise an error if compression is introduced to an object for the first time and there are usable bitmap index segments.
Example of Table Compression and Partitioning
The following statement moves and compresses an already existing partition sales_q1_1998 of table sales:
If you use the MOVE statement, the local indexes for partition sales_q1_1998 become unusable. 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. In partition pruning, the optimizer analyzes FROM and WHERE clauses in SQL statements to eliminate unneeded partitions when building the partition access list. This enables Oracle Database to perform operations only on those partitions that are relevant to the SQL statement.Oracle 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 the use of processing time, 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.
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. Refer to the table sales_range_hash earlier, partitioned by range on the column s_ salesdate and subpartitioned by hash on column s_productid, and consider
the following example:
Oracle uses the predicate on the partitioning columns to perform partition pruning as follows:
Pruning Using DATE Columns
In the earlier partitioning pruning example, the date value was fully specified as four digits for the year using the TO_DATE function, just as it was in the underlying table's range partitioning description. While this is the recommended format for specifying date values, the optimizer can prune partitions using the predicates on s_salesdate when you use other formats, as in the following example:
Although this uses the DD-MON-RR format, which is not the same as the base partition, the optimizer can still prune properly.
If you execute an EXPLAIN PLAN statement on the query, the PARTITION_START and PARTITION_STOP columns of the output table do not specify which partitions Oracle is accessing. Instead, you see the keyword KEY for both columns. The keyword KEY for both columns means that partition pruning occurs at run-time. It can also affect the execution plan because the information about the pruned partitions is missing compared to the same statement using the same TO_DATE function than the partition table definition.
Avoiding I/O Bottlenecks
To avoid I/O bottlenecks, when Oracle is not scanning all partitions because some have been eliminated by pruning, spread each partition over several devices. On MPP systems, spread those devices over multiple nodes.
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 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. Partition-wise joins can be full or partial. Oracle decides which type of join to use.
Full Partition-Wise Joins
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 100 articles in Quarter 3 of 1999" 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, as described under Granules 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 16. You can use various partitioning methods to equipartition both tables on the column customerid with 16 partitions.These methods are described in these subsections.
This is the simplest method: the customers and sales tables are both partitioned by hash into 16 partitions, on the s_customerid and c_customerid columns.This partitioning method enables full partition-wise join when the tables are joined on c_customerid and s_customerid, both representing the same customer identification number. 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.
Note:A pair of matching hash partitions is defined as one partition with the same partition number from each table. For example, with full partition-wise joins we join partition 0 ofsaleswith partition 0 ofcustomers, partition 1 ofsaleswith partition 1 ofcustomers, and so on.
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.
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 16.
Note:To guarantee an equal work distribution, the number of partitions should always be a multiple of the degree of parallelism.
In Oracle Real Application Clusters environments running on shared-nothing or MPP platforms, placing partitions on nodes is critical to achieving good scalability. To avoid remote I/O, both matching partitions should have affinity to the same node. 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.
(Composite-Hash)-HashThis 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.
For example, assume you want to partition the sales table into eight partitions by range on the column s_salesdate. Also assume you have two years and that each partition represents a quarter. Instead of using range partitioning, you can use composite partitioning to enable a full partition-wise join while preserving the partitioning on s_salesdate. Partition the sales table by range on s_ salesdate and then subpartition each partition by hash on s_customerid using 16 subpartitions for each partition, for a total of 128 subpartitions. 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.
The following 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, 16.
Hash partitions are implicit in a composite table. However, Oracle does not record them in the data dictionary, and you cannot manipulate them with DDL commands as you can range partitions.
Range and Hash Partitions of a Composite Table
(Composite-Hash)-Hash partitioning is effective because it lets you combine pruning (on s_salesdate) with a full partition-wise join (on customerid). In the previous example query, pruning is achieved by scanning only the subpartitions corresponding to Q3 of 1999, in other words, row number 3 in Figure. Oracle then joins these subpartitions with the customer table, using a full partition-wise join.
All characteristics of the hash-hash partition-wise join apply to the composite-hash partition-wise join. In particular, for this example, these two points are common to both methods:
(Composite-List)-ListThe (Composite-List)-List method resembles that for (Composite-Hash)-Hash partition-wise joins.
Composite-Composite (Hash/List Dimension)If needed, you can also partition the customer table by the composite method. For example, you partition it by range on a postal code column to enable pruning based on postal code. You then subpartition it by hash on customerid using the same number of partitions (16) to enable a partition-wise join on the hash dimension.
Range-Range and List-ListYou can also join range partitioned tables with range partitioned tables and list partitioned tables with list partitioned tables in a partition-wise manner, but this is relatively uncommon. This is more complex to implement because you must know the distribution of the data before performing the join. Furthermore, if you do not correctly identify the partition bounds so thatyou have partitions of equal size, data skew during the execution may result.
The basic principle for using range-range and list-list is the same as for using hash-hash: you must equipartition both tables. This means that the number of partitions must be the same and the partition bounds must be identical. For example, assume that you know in advance that you have 10 million customers, and that the values for customerid vary from 1 to 10,000,000. In other words, you have 10 million possible different values. To create 16 partitions, you can range partition both tables, sales on s_customerid and customers on c_ customerid. You should define partition bounds for both tables in order to generate partitions of the same size. In this example, partition bounds should be defined as 625001, 1250001, 1875001, ... 10000001, so that each partition contains 625000 rows.
Range-Composite, Composite-Composite (Range Dimension)Finally, you can also subpartition one or both tables on another column. Therefore, the range-composite and composite-composite methods on the range dimension are also valid for enabling a full partition-wise join on the range dimension.
Partial Partition-wise Joins
Oracle can perform partial partition-wise joins only in parallel. Unlike full partition-wise joins, partial partition-wise joins require you to partition only one table on the join key, not both tables. The partitioned table is referred to as the reference table. The other table may or may not be partitioned. Partial partition-wise joins are more common than full partition-wise joins.
To execute a partial partition-wise join, Oracle dynamically repartitions the other table based on the partitioning of the reference table.Once the other table is repartitioned, the execution is similar to a full partition-wise join.
The performance advantage that partial partition-wise joins have over joins in non-partitioned tables is that the reference table is not moved during the join operation. Parallel joins between non-partitioned tables require both input tables to be redistributed on the join key. This redistribution operation involves exchanging rows between parallel execution servers. This is a CPU-intensive operation that can lead to excessive interconnect traffic in Oracle Real Application Clusters environments. Partitioning large tables on a join key, either a foreign or primary key, prevents this redistribution every time the table is joined on that key. Of course, if you choose a foreign key to partition the table, which is the most common scenario,select a foreign key that is involved in many queries.
To illustrate partial partition-wise joins, consider the previous sales/customer example. Assume that sales is not partitioned or is partitioned on a column other than s_customerid . Because sales is often joined with customers on customerid, and because this join dominates our application workload, partitionsales on s_customerid to enable partial partition-wise join every time customers and sales are joined. As in full partition-wise join, you have several alternatives:
Hash/ListThe simplest method to enable a partial partition-wise join is to partition sales by hash on s_customerid. The number of partitions determines the maximum degree of parallelism, because the partition is the smallest granule of parallelism for partial partition-wise join operations.
The parallel execution of a partial partition-wise join is illustrated in the following figure, which assumes that both the degree of parallelism and the number of partitions of sales are 16. The execution involves two sets of query servers: one set, labeledset 1in Figure, scans the customers table in parallel. The granule of parallelism for the scan operation is a range of blocks.
Rows from customers that are selected by the first set, in this case all rows, are redistributed to the second set of query servers by hashing customerid. For example, all rows in customers that could have matching rows in partition P1 of sales are sent to query server 1 in the second set. Rows received by the second setof query servers are joined with the rows from the corresponding partitions in sales. Query server number 1 in the second set joins all customers rows that it receives with partition P1 of sales.
Partial Partition-Wise Join
Note:This section is based on range-hash, but it also applies for range-list partial partition-wise joins.
Considerations for full partition-wise joins also apply to partial partition-wise joins:
CompositeAs with full partition-wise joins, the prime partitioning method for the sales table is to use the range method on column s_salesdate. This is because sales is a typical example of a table that stores historical data. To enable a partial partition-wise join while preserving this range partitioning, subpartition sales by hash on column s_customerid using 16 subpartitions for each partition. Pruningand partial partition-wise joins can be used together if a query joins customers and sales and if the query has a selection predicate on s_salesdate.
When sales is composite, the granule of parallelism for a partial partition-wise join is a hash partition and not a subpartition. Again, the number of hash partitions should be a multiple of the degree of parallelism. Also, on an MPP system, ensure that each hash partition has affinity to a single node. In the previous example, the eight subpartitions composing a hash partition should have affinity to the same node.
Note:This section is based on range-hash, but it also pplies for range-list partial partition-wise joins.
RangeFinally, you can use range partitioning on s_customerid to enable a partial partition-wise join.This works similarly to the hash method, but a side effect of range partitioning is that the resulting data distribution could be skewed if the size of the partitions differs. Moreover, this method is more complex to implement because it requires prior knowledge of the values of the partitioning column that is also a join key.
Benefits of Partition-Wise Joins
Partition-wise joins offer benefits described in this section:
Reduction of Communications OverheadWhen executed in parallel, partition-wise joins reduce communications overhead. This is because, in the default case, parallel execution of a join operation by a set of parallel execution servers requires the redistribution of each table on the join column into disjoint subsets of rows. These disjoint subsets of rows are then joined pair-wise by a single parallel execution server.
Oracle can avoid redistributing the partitions because the two tables are already partitioned on the join column. This enables each parallel execution server to join a pair of matching partitions.
This improved performance from using parallel execution is even more noticeable in Oracle Real Application Clusters configurations with internode parallel execution. Partition-wise joins dramatically reduce interconnect traffic. Using this feature is for large DSS configurations that use Oracle Real Application Clusters.
Currently, most Oracle Real Application Clusters platforms, such as MPP and SMP clusters, provide limited interconnect bandwidths compared with their processing powers. Ideally, interconnect bandwidth should be comparable to disk bandwidth, but this is seldom the case. As a result, most join operations in Oracle Real Application Clusters experience high interconnect latencies without parallel execution of partition-wise joins.
Reduction of Memory RequirementsPartition-wise joins require less memory than the equivalent join operation of the complete data set of the tables being joined.In the case of serial joins, the join is performed at the same time on a pair of matching partitions. If data is evenly distributed across partitions, the memory requirement is divided by the number of partitions. There is no skew.
In the parallel case, memory requirements depend on the number of partition pairs that are joined in parallel. For example, if the degree of parallelism is 20 and the number of partitions is 100, 5 times less memory is required because only 20 joins of two partitions are performed at the same time. The fact that partition-wise joins require less memory has a direct effect on performance. For example, the join probably does not need to write blocks to disk during the build phase of a hash join.
Performance Considerations for Parallel Partition-Wise Joins
The optimizer weighs the advantages and disadvantages when deciding whether or not to use partition-wise joins.
Partitioning and Subpartitioning Columns and Keys
The partitioning columns (or subpartitioning columns) of a table or index consist of an ordered list of columns whose values determine how the data is partitioned or subpartitioned. This list can include up to 16 columns, and cannot include any of the following types of columns:
A row's partitioning key is an ordered list of its values for the partitioning columns. Similarly, in composite partitioning a row's subpartitioning key is an ordered list of its values for the subpartitioning columns. Oracle applies either the range, list, or hash method to each row's partitioning key or subpartitioning key to determine which partition or subpartition the row belongs in.
Partition Bounds for Range Partitioning
In a range-partitioned table or index, the partitioning key of each row is compared with a set of upper and lower bounds to determine which partition the row belongs in:
The partition bounds collectively define an ordering of the partitions in a table or index. The first partition is the partition with the lowest VALUES LESS THAN clause, and the last or highest partition is the partition with the highest VALUES LESS THAN clause.
Comparing Partitioning Keys with Partition Bounds
If you attempt to insert a row into a table and the row's partitioning key is greater than or equal to the partition bound for the highest partition in the table, the insert will fail.
When comparing character values in partitioning keys and partition bounds, characters are compared according to their binary values. However, if a character consists of more than one byte, Oracle compares the binary value of each byte, not of the character. The comparison also uses the comparison rules associated with the column data type. For example, blank-padded comparison is done for the ANSI CHAR data type. The NLS parameters, specifically the initialization parameters NLS_SORT and NLS_LANGUAGE and the environment variable NLS_LANG, have no effect on the comparison.
The binary value of character data varies depending on which character set is being used (for example, ASCII or EBCDIC). For example, ASCII defines the characters A through Z as less than the characters a through z, whereas EBCDIC defines A through Z as being greater than a through z. Thus, partitions designed for one sequence will not work with the other sequence. Yo
Data Warehousing Related Interview Questions
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Data Warehousing Tutorial
Data Warehousing Concepts
Physical Design In Data Warehouses
Hardware And I/o Considerations In Data Warehouses
Parallelism And Partitioning In Data Warehouses
Basic Materialized Views
Advanced Materialized Views
Overview Of Extraction, Transformation, And Loading
Extraction In Data Warehouses
Transportation In Data Warehouses
Loading And Transformation
Maintaining The Data Warehouse
Change Data Capture
Schema Modeling Techniques
Sql For Aggregation In Data Warehouses
Sql For Analysis And Reporting
Sql For Modeling
Olap And Data Mining
Using Parallel Execution
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