Beat performance bottlenecks and quick response time.
Run on clusters of cheap PC servers.
Open Source and no license fee (reduce cost).
Quickly Horizontal scalable
Distributed database
Easy replication support
Sunday, July 4, 2010
Six reasons to choose NOSQL
NOSQL
Next Generation Database Winners
Application users expects fast response time as specially I am seeing new generation doesn't tolerate 3 secs response time which is widely accepted by industry and companies having challenge in meeting customer expectations with large data sets. NOSQL is the perfect fit for this requirement. Distributed, scalable databases are desperately needed these days. There are lots of players in the NOSQL world but Cassandra/HBase is the garnered competitors.
HBase is the more robust database for a majority of use-cases. Cassandra relies mostly on Key-Value pairs for storage, with a table-like structure added to make more robust data structures possible. It’s a fact that far more people are using HBase than Cassandra at this moment, despite both being similarly recent.
HBase values strong consistency and High Availability. Cassandra values Availability and partitioning tolerance. Cassandra may be useful for storage, but not any data processing. HBase is much handier for that data processing and data ware housing.
HBase also has a nice web-based UI that you can use to view cluster status, determine which nodes store various data, and do some other basic operations. Cassandra lacks this web UI as well as a shell, making it harder to operate.
Installation:
Cassandra is only a Ruby gem install away. That’s pretty impressive. HBase we have to do quite a bit of manual configuration and installation and configuration heavy.
Availability:
Cassandra claims that “writes never fail”, whereas in HBase, if a region server is down, writes will be blocked for affected data until the data is redistributed. This rarely happens in practice, of course, but will happen in a large enough cluster. In addition, HBase has a single point-of-failure (the Hadoop NameNode), but that will be less of an issue as Hadoop evolves. HBase does have row locking, however, which Cassandra does not.
Consistency
Apps usually rely on data being accurate and unchanged from the time of access, so the idea of eventual consistency can be a problem. Cassandra, however, has an internal method of resolving up-to-dateness issues with vector clocks — a complex but workable solution where basically the latest timestamp wins. The HBase/BigTable puts the impetus of resolving any consistency conflicts on the application, as everything is stored versioned by timestamp.
Cassandra only supports one table per install. That means you can’t denormalize and duplicate your data to make it more usable in analytical scenarios. Cassandra is really more of a Key Value store than a Data Warehouse. Furthermore, schema changes require a cluster restart.
Replication:
Cassandra uses a P2P sharing model, whereas HBase (the upcoming version) employs more of a data+logs backup method, aka ‘log shipping’.
Conclusion:
If you need highly available writes with only eventual consistency, then Cassandra is a viable candidate for now. However, many apps are not happy with eventual consistency, and it is still lacking many features. Furthermore, even if writes do not fail, there is still cluster downtime associated with even minor schema changes. HBase is more focused on reads, but can handle very high read and write throughput. It’s much more Data Warehouse ready, in addition to serving millions of requests per second. The HBase integration with MapReduce makes it valuable, and versatile.
HBase is the more robust database for a majority of use-cases. Cassandra relies mostly on Key-Value pairs for storage, with a table-like structure added to make more robust data structures possible. It’s a fact that far more people are using HBase than Cassandra at this moment, despite both being similarly recent.
HBase values strong consistency and High Availability. Cassandra values Availability and partitioning tolerance. Cassandra may be useful for storage, but not any data processing. HBase is much handier for that data processing and data ware housing.
HBase also has a nice web-based UI that you can use to view cluster status, determine which nodes store various data, and do some other basic operations. Cassandra lacks this web UI as well as a shell, making it harder to operate.
Installation:
Cassandra is only a Ruby gem install away. That’s pretty impressive. HBase we have to do quite a bit of manual configuration and installation and configuration heavy.
Availability:
Cassandra claims that “writes never fail”, whereas in HBase, if a region server is down, writes will be blocked for affected data until the data is redistributed. This rarely happens in practice, of course, but will happen in a large enough cluster. In addition, HBase has a single point-of-failure (the Hadoop NameNode), but that will be less of an issue as Hadoop evolves. HBase does have row locking, however, which Cassandra does not.
Consistency
Apps usually rely on data being accurate and unchanged from the time of access, so the idea of eventual consistency can be a problem. Cassandra, however, has an internal method of resolving up-to-dateness issues with vector clocks — a complex but workable solution where basically the latest timestamp wins. The HBase/BigTable puts the impetus of resolving any consistency conflicts on the application, as everything is stored versioned by timestamp.
Cassandra only supports one table per install. That means you can’t denormalize and duplicate your data to make it more usable in analytical scenarios. Cassandra is really more of a Key Value store than a Data Warehouse. Furthermore, schema changes require a cluster restart.
Replication:
Cassandra uses a P2P sharing model, whereas HBase (the upcoming version) employs more of a data+logs backup method, aka ‘log shipping’.
Conclusion:
If you need highly available writes with only eventual consistency, then Cassandra is a viable candidate for now. However, many apps are not happy with eventual consistency, and it is still lacking many features. Furthermore, even if writes do not fail, there is still cluster downtime associated with even minor schema changes. HBase is more focused on reads, but can handle very high read and write throughput. It’s much more Data Warehouse ready, in addition to serving millions of requests per second. The HBase integration with MapReduce makes it valuable, and versatile.
Database Trend
Databases have been in use since the earliest days of electronic computing. Here is the history and where we are at currently:
1960s Navigational DBMS
1970s Relational DBMS & end of 1970s SQL DBMS
1980s Object Oriented Databases
1990s Performance enhancement through replication in an object-oriented DBMS.
2000s NoSQL databases are non-relational (Next Generation databases)
Typical modern relational databases have shown poor performance on data-intensive applications including indexing a large number of documents, serving pages on high-traffic websites and delivering streaming media.
Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontal scalable. The original intention has been modern web-scale databases, use case friendly.
1960s Navigational DBMS
1970s Relational DBMS & end of 1970s SQL DBMS
1980s Object Oriented Databases
1990s Performance enhancement through replication in an object-oriented DBMS.
2000s NoSQL databases are non-relational (Next Generation databases)
Typical modern relational databases have shown poor performance on data-intensive applications including indexing a large number of documents, serving pages on high-traffic websites and delivering streaming media.
Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontal scalable. The original intention has been modern web-scale databases, use case friendly.
Saturday, January 30, 2010
11g feature: Reference Partitioning
Reference Partitioning
Reference partitioning allows the partitioning of a table based on the partitioning scheme of the table referenced in its referential constraint. The partitioning key is resolved through an existing parent-child relationship, enforced by active primary key or foreign key constraints. This means that tables with a parent-child relationship can be logically equi-partitioned by inheriting the partitioning key from the parent table without duplicating the key columns. The logical dependency also automatically cascades partition maintenance operations, making application development easier and less error-prone.
To create a reference-partitioned table, you specify a PARTITION BY REFERENCE clause in the CREATE TABLE statement. This clause specifies the name of a referential constraint and this constraint becomes the partitioning referential constraint that is used as the basis for reference partitioning in the table.
As with other partitioned tables, you can specify object-level default attributes, and can optionally specify partition descriptors that override the object-level defaults on a per-partition basis.
CREATE TABLE orders
(order_id NUMBER(12),
order_date DATE,
order_mode VARCHAR2(8),
customer_id NUMBER(6),
order_status NUMBER(2),
order_total NUMBER(8,2),
sales_rep_id NUMBER(6),
promotion_id NUMBER(6),
CONSTRAINT orders_pk PRIMARY KEY(order_id)
)
PARTITION BY RANGE(order_date)
(PARTITION Q1_2005 VALUES LESS THAN
(TO_DATE('01-APR-2005','DD-MON-YYYY')),
PARTITION Q2_2005 VALUES LESS THAN
(TO_DATE('01-JUL-2005','DD-MON-YYYY')),
PARTITION Q3_2005 VALUES LESS THAN
(TO_DATE('01-OCT-2005','DD-MON-YYYY')),
PARTITION Q4_2005 VALUES LESS THAN
(TO_DATE('01-JAN-2006','DD-MON-YYYY')));
The example in the slide above creates a list-partitioned table called ORDERS which is range-partitioned on order_date. It is created with four partitions, Q1_2005, Q2_2005, Q3_2005, and Q4_2005. This table will be referenced in the creation of a reference partitioned table on the next slide.
CREATE TABLE order_items
( order_id NUMBER(12) NOT NULL,
line_item_id NUMBER(3) NOT NULL,
product_id NUMBER(6) NOT NULL,
unit_price NUMBER(8,2),
quantity NUMBER(8),
CONSTRAINT order_items_fk
FOREIGN KEY(order_id) REFERENCES orders(order_id)
)
PARTITION BY REFERENCE(order_items_fk);
The reference-partitioned child table ORDER_ITEMS example above is created with four partitions, Q1_2005, Q2_2005, Q3_2005, and Q4_2005, where each partition contains the order_items rows corresponding to orders in the respective parent partition.
If partition descriptors are provided, then the number of partitions described must be exactly equal to the number of partitions or sub-partitions in the referenced table. If the parent table is a composite partitioned table, then the table will have one partition for each sub-partition of its parent; otherwise the table will have one partition for each partition of its parent.
Partition bounds cannot be specified for the partitions of a reference-partitioned table. The partitions of a reference-partitioned table can be named. If a partition is not explicitly named, then it will inherit its name from the corresponding partition in the parent table, unless this inherited name conflicts with one of the explicit names given. In this case, the partition will have a system-generated name.
Partitions of a reference-partitioned table will co-locate with the corresponding partition of the parent table, if no explicit tablespace is specified for the reference-partitioned table's partition.
Reference partitioning allows the partitioning of a table based on the partitioning scheme of the table referenced in its referential constraint. The partitioning key is resolved through an existing parent-child relationship, enforced by active primary key or foreign key constraints. This means that tables with a parent-child relationship can be logically equi-partitioned by inheriting the partitioning key from the parent table without duplicating the key columns. The logical dependency also automatically cascades partition maintenance operations, making application development easier and less error-prone.
To create a reference-partitioned table, you specify a PARTITION BY REFERENCE clause in the CREATE TABLE statement. This clause specifies the name of a referential constraint and this constraint becomes the partitioning referential constraint that is used as the basis for reference partitioning in the table.
As with other partitioned tables, you can specify object-level default attributes, and can optionally specify partition descriptors that override the object-level defaults on a per-partition basis.
CREATE TABLE orders
(order_id NUMBER(12),
order_date DATE,
order_mode VARCHAR2(8),
customer_id NUMBER(6),
order_status NUMBER(2),
order_total NUMBER(8,2),
sales_rep_id NUMBER(6),
promotion_id NUMBER(6),
CONSTRAINT orders_pk PRIMARY KEY(order_id)
)
PARTITION BY RANGE(order_date)
(PARTITION Q1_2005 VALUES LESS THAN
(TO_DATE('01-APR-2005','DD-MON-YYYY')),
PARTITION Q2_2005 VALUES LESS THAN
(TO_DATE('01-JUL-2005','DD-MON-YYYY')),
PARTITION Q3_2005 VALUES LESS THAN
(TO_DATE('01-OCT-2005','DD-MON-YYYY')),
PARTITION Q4_2005 VALUES LESS THAN
(TO_DATE('01-JAN-2006','DD-MON-YYYY')));
The example in the slide above creates a list-partitioned table called ORDERS which is range-partitioned on order_date. It is created with four partitions, Q1_2005, Q2_2005, Q3_2005, and Q4_2005. This table will be referenced in the creation of a reference partitioned table on the next slide.
CREATE TABLE order_items
( order_id NUMBER(12) NOT NULL,
line_item_id NUMBER(3) NOT NULL,
product_id NUMBER(6) NOT NULL,
unit_price NUMBER(8,2),
quantity NUMBER(8),
CONSTRAINT order_items_fk
FOREIGN KEY(order_id) REFERENCES orders(order_id)
)
PARTITION BY REFERENCE(order_items_fk);
The reference-partitioned child table ORDER_ITEMS example above is created with four partitions, Q1_2005, Q2_2005, Q3_2005, and Q4_2005, where each partition contains the order_items rows corresponding to orders in the respective parent partition.
If partition descriptors are provided, then the number of partitions described must be exactly equal to the number of partitions or sub-partitions in the referenced table. If the parent table is a composite partitioned table, then the table will have one partition for each sub-partition of its parent; otherwise the table will have one partition for each partition of its parent.
Partition bounds cannot be specified for the partitions of a reference-partitioned table. The partitions of a reference-partitioned table can be named. If a partition is not explicitly named, then it will inherit its name from the corresponding partition in the parent table, unless this inherited name conflicts with one of the explicit names given. In this case, the partition will have a system-generated name.
Partitions of a reference-partitioned table will co-locate with the corresponding partition of the parent table, if no explicit tablespace is specified for the reference-partitioned table's partition.
Hash Partitioning/How Cardinality Affects Distribution
Hash partitioning provides a method of evenly distributing data across a specified number of partitions. Rows are mapped into partitions based on a hash value of the partitioning key and is a better choice than range partitioning when:
a.You do not know beforehand how much data will map into a given range
b.Sizes of range partitions would differ quite substantially
c.Partition pruning and partition-wise joins on a partitioning key are important
d.Range partitioning would cause the data to be undesirably clustered
In 11g hash partitioning can also be used:
a.To enable partial or full parallel partition-wise joins with very likely equi-sized partitions.
b.To distribute data evenly among the nodes of an MPP platform that uses Oracle Real Application Clusters. As a result, you can minimize interconnect traffic
when processing internode parallel statements.
c.To use partition pruning and partition-wise joins according to a partitioning
key that is mostly constrained by a distinct value or value list.
d.To randomly distribute data to avoid I/O bottlenecks if you do not use
a storage management technique that stripes and mirrors across all available
devices.
The concepts of splitting, dropping or merging partitions do not apply to hash partitions. Instead, hash partitions can be added and coalesced and with hash partitioning, pruning is only possible using equality or IN-list predicates.
The number of partitions should always be a power of two (2, 4, 8, and so on)to obtain the most even data distribution.
Cardinality is important for choosing a key for hash partitioning. Oracle recommends that you do not use hash partitioning on key columns with low cardinality (columns in which the number of distinct values is small compared to
the number of rows in the table.)
The hash function works best with a LARGE number of values. Rows are mapped into partitions based on the hash value of the partitioning key. A primary key is a very good hash key because it has a wide range a values.
For example, if you had a hash partitioned table with 8 hash partitions, and a key column with only 10 distinct values, you could encounter the following:
CREATE TABLE dept (deptno NUMBER, deptname VARCHAR(32))
PARTITION BY HASH(deptno) PARTITIONS 8;
Partition DEPTNO Column Values
----------- ---------------------
hash_part1 1,2,3
hash_part2 4,5,6,
hash_part3 7
hash_part4 8
hash_part5
hash_part6
hash_part7 9
hash_part8 10
You would have a scenario where some of your hash partitions would contain a high number of distinct values and your hash partitions would be widely different sizes. It is even possible that some hash partitions could contain zero distinct values and others will contain three or more distinct values. One of the reasons to use hash partitioning is to have evenly sized partitions. That's why we recommend that you do not use hash partitioning when low cardinality is a factor.
In general, the hash-partitioning key should be unique or near unique. Since a table may contain several near-unique keys and thus several candidates for hash
partitioning, the other consideration for choosing the partitioning key is the access profile. Hash partitioning can improve the performance of single-key lookups.
For example:
SELECT * FROM emp WHERE empno = 123 <-----single key lookup SELECT * FROM emp WHERE sal > 5000 <------range lookup
If you create a local index for this table, the database constructs the index so that it is equipartitioned with the underlying table.
The database also ensures that the index is maintained automatically when maintenance operations are performed on the underlying table. The following is an example of creating a local index on the table dept: CREATE INDEX loc_dept_ix ON dept(deptno) LOCAL;
You can optionally name the hash partitions and tablespaces into which the local index partitions are to be stored, but if you do not do so, the database uses the name of the corresponding base partition as the index partition name, and stores the index partition in the same tablespace as the table partition.
Hash partitioned global indexes can improve the performance of indexes where a small number of leaf blocks in the index have high contention in multiuser OLTP
environments. Queries involving the equality and IN predicates on the index partitioning key can efficiently use hash-partitioned global indexes.
The syntax for creating a hash partitioned global index is similar to that used
for a hash partitioned table. For example, the following statement creates a
hash-partitioned global index:
CREATE INDEX hgidx ON tab (c1,c2,c3) GLOBAL
PARTITION BY HASH (c1,c2)
(PARTITION p1 TABLESPACE tbs_1,
PARTITION p2 TABLESPACE tbs_2,
PARTITION p3 TABLESPACE tbs_3,
PARTITION p4 TABLESPACE tbs_4);
a.You do not know beforehand how much data will map into a given range
b.Sizes of range partitions would differ quite substantially
c.Partition pruning and partition-wise joins on a partitioning key are important
d.Range partitioning would cause the data to be undesirably clustered
In 11g hash partitioning can also be used:
a.To enable partial or full parallel partition-wise joins with very likely equi-sized partitions.
b.To distribute data evenly among the nodes of an MPP platform that uses Oracle Real Application Clusters. As a result, you can minimize interconnect traffic
when processing internode parallel statements.
c.To use partition pruning and partition-wise joins according to a partitioning
key that is mostly constrained by a distinct value or value list.
d.To randomly distribute data to avoid I/O bottlenecks if you do not use
a storage management technique that stripes and mirrors across all available
devices.
The concepts of splitting, dropping or merging partitions do not apply to hash partitions. Instead, hash partitions can be added and coalesced and with hash partitioning, pruning is only possible using equality or IN-list predicates.
The number of partitions should always be a power of two (2, 4, 8, and so on)to obtain the most even data distribution.
Cardinality is important for choosing a key for hash partitioning. Oracle recommends that you do not use hash partitioning on key columns with low cardinality (columns in which the number of distinct values is small compared to
the number of rows in the table.)
The hash function works best with a LARGE number of values. Rows are mapped into partitions based on the hash value of the partitioning key. A primary key is a very good hash key because it has a wide range a values.
For example, if you had a hash partitioned table with 8 hash partitions, and a key column with only 10 distinct values, you could encounter the following:
CREATE TABLE dept (deptno NUMBER, deptname VARCHAR(32))
PARTITION BY HASH(deptno) PARTITIONS 8;
Partition DEPTNO Column Values
----------- ---------------------
hash_part1 1,2,3
hash_part2 4,5,6,
hash_part3 7
hash_part4 8
hash_part5
hash_part6
hash_part7 9
hash_part8 10
You would have a scenario where some of your hash partitions would contain a high number of distinct values and your hash partitions would be widely different sizes. It is even possible that some hash partitions could contain zero distinct values and others will contain three or more distinct values. One of the reasons to use hash partitioning is to have evenly sized partitions. That's why we recommend that you do not use hash partitioning when low cardinality is a factor.
In general, the hash-partitioning key should be unique or near unique. Since a table may contain several near-unique keys and thus several candidates for hash
partitioning, the other consideration for choosing the partitioning key is the access profile. Hash partitioning can improve the performance of single-key lookups.
For example:
SELECT * FROM emp WHERE empno = 123 <-----single key lookup SELECT * FROM emp WHERE sal > 5000 <------range lookup
If you create a local index for this table, the database constructs the index so that it is equipartitioned with the underlying table.
The database also ensures that the index is maintained automatically when maintenance operations are performed on the underlying table. The following is an example of creating a local index on the table dept: CREATE INDEX loc_dept_ix ON dept(deptno) LOCAL;
You can optionally name the hash partitions and tablespaces into which the local index partitions are to be stored, but if you do not do so, the database uses the name of the corresponding base partition as the index partition name, and stores the index partition in the same tablespace as the table partition.
Hash partitioned global indexes can improve the performance of indexes where a small number of leaf blocks in the index have high contention in multiuser OLTP
environments. Queries involving the equality and IN predicates on the index partitioning key can efficiently use hash-partitioned global indexes.
The syntax for creating a hash partitioned global index is similar to that used
for a hash partitioned table. For example, the following statement creates a
hash-partitioned global index:
CREATE INDEX hgidx ON tab (c1,c2,c3) GLOBAL
PARTITION BY HASH (c1,c2)
(PARTITION p1 TABLESPACE tbs_1,
PARTITION p2 TABLESPACE tbs_2,
PARTITION p3 TABLESPACE tbs_3,
PARTITION p4 TABLESPACE tbs_4);
Sunday, November 29, 2009
Content management databases
Content Management databases: There are good content management database in market and used wide across several companies like yahoo, Facebook, Amazon.
Thrudb is a set of simple services built on top of the Facebook Apache Thrift framework that provides indexing and document storage services for building and scaling websites. Its purpose is to offer web developers flexible, fast and easy-to-use services that can enhance or replace traditional data storage and access layers.
MongoDB
MongoDB is an open source document-oriented database written in the C++ programming language.
MongoDB is designed for problems without heavy transactional requirements that aren't easily solved by traditional RDBMSs, including problems which require the database to span many servers.
Thrudb is a set of simple services built on top of the Facebook Apache Thrift framework that provides indexing and document storage services for building and scaling websites. Its purpose is to offer web developers flexible, fast and easy-to-use services that can enhance or replace traditional data storage and access layers.
Thrudb - High Level Features
- Client libraries for most languages
- Incremental backups and redo logging
- Multiple storage backends (BerkeleyDB, Disk, MySQL, S3 included)
- Memcache and Spread integration.
- Built for horizontal scalability
- Simple and powerful search service
Thrudb - Services
- Thrudoc - Document storage service
- Thrudex - Indexing and search service
- Thruqueue - Persistent message queue service
- Throxy - Scaling service
StrokeDB
Minimalistic modular database engine.StrokeDB stores a set of versioned documents identified by UUID. A document is a hash-like container of slots: flat set of values tagged with string keys. Slots store arbitrary serializable values (most common types are booleans, numbers, strings, arrays, numbers, time).
The concept.
1. Every repository is identified by UUID.
2. Every repository writes a log of commits.
3. Commit tuples:
(timestamp, "store", uuid, version)
(timestamp, "pull", repo_uuid, repo_timestamp)
4. When you pull from a repository:
1. Find out the latest timestamp in your history (index: repo_uuid -> repo_timestamp)
2. If there is not timestamp yet, pull the whole log.
3. If there is a timestamp for a repository UUID, pull the tail of the log.
4. For each "store" record: fetch the version.
5. For each "pull" record - add to a deferred list of repositories waiting for update.
6. When whole log is fetched, fetch deferred repositories. We have two options here:
1. Fetch from the same repository we've been fetching from few moments ago (say, fetch B log from A)
2. Or, fetch directly from the desired repository (B log from B repository)
MongoDB
MongoDB is an open source document-oriented database written in the C++ programming language.
MongoDB is designed for problems without heavy transactional requirements that aren't easily solved by traditional RDBMSs, including problems which require the database to span many servers.
CouchDB
- A document database server, accessible via a RESTful JSON API.
- Ad-hoc and schema-free with a flat address space.
- Distributed, featuring robust, incremental replication with bi-directional conflict detection and management.
- Query-able and index-able, featuring a table oriented reporting engine that uses Javascript as a query language.
Sunday, November 8, 2009
Oracle flash drive recommendations
Oracle flash drive recommendations
EFD-friendly DB workloads | Not as cost-effective on EFD |
Random reads B-Tree leaf access ROWID look up into Table Access to out-of-line LOB Access to overflowed row Index scan over Unclustered Table Compression: Increases I/O intensity (IOPS/GB) Serial reads Random writes Row update by PK Index maintenance Reduce checkpoint interval TEMP: Sort areas and Intermediate tables Sequentially read and written but I/O done in 1 MB units: not enough to amortize seeks Lower Latency: Get In, Get Out | Redo log files Sequentially read and written and commit latency already handled by cache in storage controller Undo table space Sequentially written, randomly read by flashBack. But reads are for recently written data that is likely to still be in the buffer cache Large table scans Buffer pools with lots of writes Mismatch between read and write latency characteristics of EFDs can cause unwanted “Free Buffer Waits”. Buffer pool tuning is necessary after deploying EFDs |
Reference from Oracle Openworld 2008 presentation
Redo Logs on EFDs? (or not)
It is a common misconception that Oracle online redo logs will benefit by moving them on EFDs, whereas all the experimental data indicates the opposite position. Testing has shown that moving redo logs on to EFDs results in a low percentage of improvement. It is better to leave them on the write cache backed Fibre Channel drives rather than moving them on to EFDs, thereby using EFDs for other read intensive parts of the database like indexes or data.
Oracle TEMP tablespace on EFDs
Oracle uses this space mainly for data aggregations and sorting. When the database engine cannot fit the sorts in memory, they will be spilled on to disk for storing intermediary results. Oracle typically does large sequential I/Os against these tablespaces in the context of single user. When multiple users are performing concurrent sorts on these tablespaces, the I/O turns out to be largely random in nature. Even though EFDs do not provide as much benefit for large random I/O as they provide to small random operations, still they are far ahead of what regular rotation Fibre Channel drives can deliver. Depending on the availability of space on EFDs, Oracle applications will be benefited by moving the temporary tablespaces to them. Temporary tablespace files should only be moved to EFDs after all the I/O intensive parts have been moved to them.
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