Dragon age 2 increase text size

Foods to improve sex drive in males

Second option (innodb_flush_log_at_trx_commit=0) is the most risky one but also the fastest. Both redo logs and InnoDB buffer pool work together - buffer pool stores data that was actually modified while redo logs stores information that describe the kind of modifications that were applied - this combines in-memory write cache with durable storage that allows to recreate modifications should memory be erased by a MySQL restart. As you can see in the screenshot, we can check if the dirty pages are on a stable level and how large it is when compared to the used part of the buffer pool (which is, for probably most of the cases, same as the total size of the buffer pool - datasets tend to be larger than memory and eventually the whole buffer pool will be filled with data). In the graph above, there’s nothing really concerning - dirty pages are on a stable level, not even close to the 75% of the total buffer pool’s size.
We took a look at the state of the buffer pool, let’s now check the other side of the equation and see if we are facing any issues with InnoDB redo logs. Another good place to look for are the status counters called innodb_buffer_pool_pages_flushed and innodb_buffer_pool_pages_lru_flushed.
This data should help you to determine what kind of flushing is the most common under your workload. In general, a good way to calculate redo log size is to see how much data InnoDB writes to the log. As stated at the beginning, we did not cover all of the details in this post - for example we did not mention InnoDB’s change buffer, we did not explain how double-write buffer works. The parallel query feature is capable of dramatic response time reductions for data-intensive operations on very large decision support databases. See your operating system-specific Oracle documentation for more information about tuning while using the parallel query feature. The parallel query feature is useful for queries that access a large amount of data by way of large table scans, large joins, the creation of large indexes, bulk loads, aggregation, or copying. If any one of these conditions is not true for your system, the parallel query feature may not significantly help performance. The three basic steps for tuning the parallel query are outlined in the following sections. On some platforms you may need to set operating system parameters which control the total amount of virtual memory available, summed across all processes. As a general guideline for memory sizing, note that each process needs address space big enough for its hash joins.
Memory available for DSS queries comes from process memory, which in turn comes from virtual memory. Virtual memory is typically more than physical memory, but should not generally exceed twice the size of the physical memory less the SGA size. Tune the following parameters to ensure that resource consumption is optimized for your parallel query needs. The hash area does not cache blocks in the buffer cache; even low values of HASH_AREA_SIZE will not cause this to occur. The default value of OPTIMIZER_PERCENT_PARALLEL is 0, which parallelizes the plan which uses the least resource, if possible.
Note: Selecting a single record from a table, if there is an appropriate index, can be done very quickly and does not require parallelism.
Recommended Value: 2 * CPUs * number_of_concurrent_usersMost queries need at most twice the number of query server processes as the maximum degree of parallelism attributed to any table in the query.
For example, if you have determined that the maximum number of concurrent query servers that your machine can manage is 100, you should set PARALLEL_MAX_SERVERS to 100. Consider decreasing PARALLEL_MIN_SERVERS if fewer query servers than this value are typically busy at any given time. Increase the initial value of this parameter to provide space for a pool of message buffers that parallel query servers can use to communicate with each other. On Oracle Parallel Server, there can be multiple CPUs in a single node, and parallel query can be performed across nodes.
Note that the degree of parallelism on OPS is expressed by the number of CPUs per node multiplied by the number of nodes.
Sample Range: 256K to 4MThis parameter specifies the amount of memory to allocate per query server for sort operations.
Recommended Value: HASHWhen set to HASH, this parameter causes the NOT IN operator to be evaluated in parallel using a parallel hash anti-join.
As illustrated above, the SQL IN predicate can be evaluated using a join to intersect two sets.
Alternatively, the SQL NOT IN predicate can be evaluated using an anti-join to subtract two sets.
Recommended Value: TRUEThis parameter enables hash joins, which can be much faster than sort merge joins. Recommended Value: TRUEThis parameter enables optimization of UNION ALL views in order to provide key range partitioning and partition skipping. Recommended Value: 8K or 16KThe database block size must be set when the database is created.
Recommended Value: AUTOThis parameter causes the buffer cache to be bypassed for the writing of sort runs.
Serial and parallel queries which involve serial table scans (but not parallel table scans), and all index lookups, use the buffer cache for both the index and the table which the index references.
If paging is high, it is a symptom that the relationship of memory, users, and query servers is out of balance. Asynchronous operations are currently supported with parallel table scans and hash joins only.
This section describes how to tune the physical database layout for optimal performance of parallel query. The case study in this chapter illustrates how to create, load, index, and analyze a large fact table, partitioned using partition views, in a typical star schema. To stripe data during load, use the FILE= clause of parallel loader to load data from multiple load sessions into different files in the tablespace. The operating system or volume manager can perform striping (OS striping), or you can manually perform striping for parallel operations. See Also: For MPP systems, see your platform-specific Oracle documentation regarding the advisability of disabling disk affinity when using operating system striping. For a discussion of media recovery issues, see "Backup and Recovery of the Data Warehouse" on page 6-8. With the partition view feature you can use the UNION ALL construct to partition a large table into several smaller tables and make the partitioning transparent to queries using a view.
When combined with existing features and a few tips and techniques, partition views provide a flexible and powerful partitioning capability.
The case study in this chapter illustrates how to use a partition view for maximum query performance, not necessarily for minimum downtime in the event of disk failure. Here, each partition is spread across one third of the disks in the tablespace so that loss of a single disk causes 4 out of 12 partitions to become unavailable. Alternatively, partitions may be assigned to disks such that a disk failure takes out a single partition, and surviving partitions remain available.
Oracle automatically computes the default parallel degree of a table as the minimum of the number of disks storing the table and the number of CPUs available.
Note that the degree of parallelism for a partition view is conservatively set by default to be the maximum of the degrees of all partitions (not the sum). This section describes how to load a large table, such as the FACT table in a decision support database.
We use 4 gigabyte datafiles, although many operating systems impose a 2 gigabyte file size limit.
We make the INITIAL extent size small because the first extent, allocated when each object in the tablespace is created, is not used by the parallel loader. In all cases other than parallel load, INITIAL and NEXT should normally be set to the same value. In Release 7.3 objects can have an unlimited number of extents provided you have set the COMPATIBLE system parameter and use the MAXEXTENTS keyword on the CREATE or ALTER command for the tablespace or object. Create as many datafiles as the degree of parallelism you will use for creating and loading objects in the tablespace.
Note: Although this example shows parallel load used with partition views, the two features can be used independent of one another. The final step in creating a partition view is to add check constraints to the partitions so that the optimizer can skip partitions that are not needed to answer specific queries. For optimal space management performance you can use the dedicated temporary tablespaces available in release 7.3. Temporary extents should normally be all the same size (to avoid fragmentation), and smaller than permanent extents. Temporary extents should be smaller than permanent extents because there are more demands for temporary space, and parallel processes or other queries running concurrently must share the temporary tablespace. At the same time, temporary extents should be large enough that processes do not have to spend all their time waiting for space.
Operating system striping is an alternative technique you can use with temporary tablespaces. Because the dictionary sees the size as 1K, which is less than the extent size, the bad file will never be accessed. See Also: For MPP systems, see your platform-specific documentation regarding the advisability of disabling disk affinity when using operating system striping. Create at least as many files in the tablespace as the degree of parallelism used to create indexes in the tablespace. If OS striping is used, we can choose to create indexes one at a time using parallel index creation for each one. The PARALLEL clause directs use of the default parallelism (10) to scan facts and to sort and build the index. The UNRECOVERABLE clause specifies that no redo log records are to be written when building the index.
It is worthwhile computing or estimating with a larger sample size the indexed columns and indexes themselves, rather than the measure data. Key to parallel query tuning is an understanding of the relationship between memory requirements, number of users (processes) a system can support, and maximum number of query servers. In considering the maximum number of processes a system can support, it is useful to divide the processes into three classes, based on their memory requirements. In general, if max_processes is much bigger than the number of users, you can consider running parallel queries. These processes require the fixed overhead needed by a low memory process, plus one or more sort areas, depending on the query. Look at the EXPLAIN PLAN output for the query to identify the number and type of joins, and the number and type of sorts.
High memory processes include one or more hash joins; or a combination of one or more hash joins with large sorts. In summary, the amount of hash join memory for a query equals parallel degree multiplied by hash area size, multiplied by the minimum of either 2, or the number of hash joins in the query.
See Also: "Optimizing Join Statements" on page A-37 for a comparison of hash joins and sort merge joins. Your system may be able to perform acceptably even if oversubscribed by 60%, if on average not all of the processes are performing hash joins concurrently. On average, no more than 5% of the time should be spent simply waiting in the operating system on page faults. If wait time for paging devices exceeds 5%, it is a strong indication that you must reduce memory requirements. Note: You must verify that a particular degree of oversubscription will be viable on your system by monitoring the paging rate and making sure you are not spending more than a very small percent of the time waiting for the paging subsystem. If every query performs a hash join and a sort, the high memory requirement limits the number of processes you can have.
You can disable the hash join capability and explicitly enable it for important hash joins you want to run in batch.
In general there is a trade-off between parallelism for fast single user response time, and efficient use of resources for multiple users. The penalty for taking such an approach is that when a single query happens to be running, the system will use just half the CPU resource of the 10 CPU machine. To determine whether your system is being fully utilized, you can use one of the graphical system monitors which are available on most operating systems.
The examples in this section show how to evaluate the relationship between memory, users, and query servers, and balance the formula given in Figure 18-8. Assume your system has 1G of memory, and that your users perform ad hoc joins with 3 or more tables. Remember that every parallel, hash, or sort merge join query takes a number of query servers equal to twice the degree of parallelism, and often each individual process of a parallel query uses a lot of memory. Notice the trade-off above: by reducing memory per process by a factor of 16, you can increase the number of concurrent users by a factor of 16.
You might plan for 10 analysts running sequential queries that use complex hash joins accessing a large amount of data. Finally, to support hundreds of users doing low memory processes at about 0.5MB apiece, you might reserve 200MB. You might consider it safe to oversubscribe at 50% because of the infrequent batch jobs during the day. Consider a system with 2 gigabytes of memory and 10 users who want to run intensive DSS parallel queries concurrently and still have good performance.
With only 5 users doing large hash joins, each process would get over 16 MB of hash area, which would be fine.
If such a system needs to support 1000 users who must all run big queries you must evaluate the situation carefully. Given the organization's resources and business needs, is it reasonable for you to upgrade your system's memory? Accept the fact that the system will actually support a limited number of users doing big hash joins. Expect to support the 1000 users doing index lookups and joins that do not require large amounts of memory. This section describes space management issues that come into play when using the parallel query.
These issues become particularly important for parallel query running on a parallel server, where tuning becomes more critical the more nodes involved. If you are unable to allocate extents for various reasons, you can recoalesce the space by using the ALTER TABLESPACE COALESCE SPACE command.
Alternatively, for data which is mostly read-only, assign very few hashed PCM locks (for example, 2 shared locks) to each data file.

If you want DBA or fine grain locking, group together the blocks which are controlled by each lock, using the !
For example, on a read-only database with a data warehousing application's query-only workload, you might create 500 PCM locks on the SYSTEM tablespace in file 1, then create 50 more locks to be shared for all the data in the other files. Oracle computes a target degree of parallelism by examining the maximum of the degree for each table and other factors, before runtime. The parallel query feature assigns each instance a unique number, which is determined by the INSTANCE_NUMBER initialization parameter. When multiple concurrent queries are running on a single node, load balancing is done by the operating system.
For a parallel server, however, no single operating system performs the load balancing: instead, the parallel query feature performs this function. If a query requests more than one instance, allocation priorities involve table caching and disk affinity. Thus, if there are 5 query servers, it is advantageous for them to run on as many nodes as possible.
Some Oracle Parallel Server platforms use disk affinity: processes are allocated on instances that are closest to the requested data. With disk affinity, Oracle tries to allocate query servers for parallel table scans on the instances which own the data. Disk affinity is used for parallel table scans and parallel temporary tablespace allocation, but is not used for parallel table creation or parallel index creation. In the following example of disk affinity, table T is distributed across 3 nodes, and a full table scan on table T is being performed. If there are two concurrent queries against table T, each requiring 3 instances (and enough processes are available on the instances for both queries), then both queries will use instances 1, 2, and 3.
Buffer Pool Extension is one of new features of SQL Server 2014 to increase SQL Server database performance by increasing amount of cache that SQL Server can use. Buffer pool extension enables database administrators to integrate random access memory like SSD disks or flash disks to SQL Server database engine buffer pool without any change on server, server configuration or database applications.
Please note that Buffer Pool Extension feature in SQL Server 2014 is only available on 64-bit Enterprise Edition and 64-bit Standard Edition of SQL Server 2014. Make sure your disk is NTFS file formatted instead of FAT32 file format otherwise you will not be able to define buffer pool extention with size over 4 GB. Since for demonstration I used FAT32 flash disk and set file size for SQL Server 2014 buffer pool extension to 5 GB which is over limit, SQL Server will throw following error message.
And please keep the official SQL syntax for SQL Server buffer pool extension configuration command. In some SQL Server tutorials, the SQL syntax used to enable buffer pool extension differs than the RTM release of SQL Server 2014.
I've formatted my flash disk using NTFS and ready to define a bigger memory area for SQL Server 2014 buffer pool extension configuration.
Run following SQL Server configuration command to enable buffer pool extension on target disk with specified file name and size. Please note that size of the cache can be defined as KB, MB or GB and the minimum size can be configured as the size of Max Server Memory. On the other hand the maximum size is limited to 32 times of the size of SQL Server Max Server Memory.
If you define the cache size less than the configured Max Server Memory for the target SQL Server 2014 instance, the SQL engine will not allow buffer pool extension creation with below error message. Buffer pool extension size must be larger than the current memory allocation threshold 5120 MB. SQL Server database administrators can check buffer pool extension configuration on a SQL Server 2014 instance by querying sys.dm_os_buffer_pool_extension_configuration system view. The current size of the buffer pool extension is listed as KB, SQL developers or a DBA can display buffer pool file size in MB or GB using the following query on sys.dm_os_buffer_pool_extension_configuration dynamic management view (DMV). To manage buffer pool extensions, an other dynamic management view is sys.dm_os_buffer_descriptors. After database administrator has defined a new buffer pool extension and enabled on the SQL Server 2014 instance, you can query sys.dm_os_buffer_descriptors DMV to see what is placed in the buffer pool extension file. If you set the buffer pool extension to OFF which means disable the SQL Server buffer pool extension usage, first the state of the related buffer pool is set to 1 which means disable is in progress. When the disable is in progress, database administrators can not enable another buffer pool extension on the server. Attempt to enable buffer pool extension when in state BUFFER POOL EXTENSION CLEAN PAGE CACHING DISABLE IN PROGRESS is not allowed.
The turn off statement was cancelled during execution, then in system DMV although I could see the buffer pool extension file I could delete it manually but can not enable a new one or can not delete the existing one from SQL. I was able to stop and start the SQL Server service so I quickly restart SQL Server service. When the server is up, I see that the bpe file (buffer pool extension) file is recreated on the removable disk again and the state is seen as enabled.
After I execute Alter Server Configuration command for disabling the buffer pool again, I could successfully remove the cache file from disk and from SQL Server configuration.
MySQL, historically, was not very good in this area - flushing caused bumps and spikes in the workload, and kernel mutex was wrecking havoc in the overall stability.
It’s a long story, but necessary to be told in order to give you some background in regards to the metrics we’ll be discussing later. The change is written to in-memory InnoDB log buffer and to the InnoDB redo log, but it is not flushed to disk immediately but rather, once per second (approximately). With such setting there are no writes to InnoDB redo log after commit - data is stored only in InnoDB’s log buffer and flushed to disk every second. This is a file or a set of files used by InnoDB to store data about modifications before they’re pushed further to tablespaces. Magnetic hard disks have a well known pattern - they are nice and fast for sequential reads or writes but much worse when the access pattern is random. But we still have those mechanisms in place and we need to know about them in order to understand InnoDB behavior. What is described above is flushing caused by some factors like high number of the dirty pages or InnoDB error logs getting full but there’s also some more “regular” flushing.
Those graphs are not really meaningful without putting them in the context of your hardware’s limitations - this is why proper benchmarking of the server before putting it into production is so important. If we see mainly thousands of ‘innodb_buffer_pool_pages_flushed’ per second and the graph seems to be spiky, you may then want to create a bit of room for InnoDB by increasing InnoDB redo log’s size - this can be done by changing innodb_log_file_size. A rule of thumb is that the redo log should be able to accommodate one hour worth of writes. It is not reviewed in advance by Oracle and does not necessarily represent the opinion of Oracle or any other party.
The first half of this chapter outlines three basic parallel query tuning steps to get you up and running. For best results, start with an initialization file that is appropriate for the intended application. The recommended settings are guidelines for a large data warehouse (more than 100 gigabytes) on a typical high-end shared memory multiprocessor with one or two gigabytes of memory. A dominant factor in heavyweight decision support (DSS) queries is the relationship between memory, number of processes, and number of hash join operations.
Total virtual memory should be somewhat more than available real memory, which is the physical memory minus the size of the SGA.
If you make it many times more than real memory, the paging rate may go up when the machine is overloaded at peak times. Each process that performs a parallel hash join uses an amount of memory equal to HASH_AREA_SIZE. OPTIMIZER_PERCENT_PARALLEL encourages the optimizer to use plans that have low response time because of parallel execution, even if total resource used is not minimized. Note that if a database's users start up too many concurrent queries, Oracle may run out of query servers. Next determine how many query servers the average query needs, and how many queries are likely to be executed concurrently. Whereas SMP systems use 3 buffers for connection, 4 buffers are used to connect between instances on Oracle Parallel Server. If memory is abundant on your system, you can benefit from setting SORT_AREA_SIZE to a large value.
If the sort area size is smaller than the amount of data to sort, then the sort will spill to disk, creating sort runs. The cumulative sort area adds up fast because each parallel query server can allocate this amount of memory for each sort.
Without this parameter set to HASH, NOT IN is evaluated as a (sequential) correlated subquery.
To be sure that you are getting the full benefit of the latest performance features, set this parameter equal to the current release. Specifically, CHECK CONSTRAINTS and view predicates are combined with predicates in user queries to skip over partitions that are not needed to answer the user query. Many platforms limit the number of bytes read to 64K, limiting the effective maximum for an 8K block size to 8. Reading through the buffer cache may result in greater path length, excessive memory bus utilization, and LRU latch contention on SMPs.
To this end, it presents a case study showing how to prepare a simple database for parallel query. It supports multiple users running sequentially as well as single users running in parallel.
This requires more DBA planning and effort to set up, and may yield better performance if only a single query is running. With the declining price of disks, mirroring can provide an effective alternative solution to backups and log archival.
This may affect your database restore time to a point that RAID5 performance is unacceptable.
For example, a new partition can be added, an existing partition can be reorganized, or an old partition can be dropped with less than a second of interruption to a read-only application.
Direct update of partition views is not supported: updates can be coded to work against the underlying partitions.
In the case of parallel load, make the NEXT extent size large enough to keep the number of extents reasonable, and to avoid excessive overhead and serialization due to data dictionary bottlenecks.
Use a scripting language like csh or perl to multiple instances of Server Manager or SQL*Plus in line mode. For example, if a query scans rows in which dim_2 is between January 1 and February 13, you only need to scan fact_1 and fact_2. As with the TSfacts tablespace, we first add a single datafile and later add 29 more in parallel. Although temporary tablespaces use less overhead than permanent tablespaces when allocating and freeing a new extent, obtaining a new temporary extent is not completely free of overhead. Eventually, you may wish to recreate the tablespace because Oracle allows a maximum of 1022 files in the database.
The considerations for creating the index tablespace are similar to those for other tablespaces. Creating all indexes concurrently in parallel would probably overload the capacity of the machine.
Although this speeds up index creation significantly, a media recovery strategy that relies on backups and archived log files will require the DBA to re-issue the CREATE INDEX commands if a disk in Tsidx fails after indexes are created but before they are backed up. Use the COMPUTE option of the ANALYZE command if possible (it may take quite some time and a large amount of temporary space).
The measure data is not used as much: most of the predicates and critical optimizer information comes from the dimensions. You are spending more resources to get good statistics on high value columns (indexes and join columns), and getting baseline statistics for the rest of the data.
The goal is to obtain the dramatic performance enhancement made possible by parallelizing certain operations, and by using hash joins rather than sort merge joins. If max_processes is considerably less than the number of users, you must consider other alternatives, such as those described in the following section.
For example, a typical sort merge join would sort both its inputs--resulting in two sort areas. Total memory required, minus the SGA size, can be multiplied by a factor of 1.2, to allow for 20% oversubscription.
Users might then try to use more than the available memory, so you must monitor paging activity in such a situation. This could mean reducing the memory required for each class of process, or reducing the number of processes in memory-intensive classes.
Not only can you adjust the number of queries that run in parallel, but you can also adjust the degree of parallelism with which queries run. This places a system-level limit on the total amount of parallelism, and is easy to administer.
Rather than reducing parallelism for all operations, you may be able to schedule large parallel batch jobs to run with full parallelism one at a time, rather than concurrently.
You can move a process from the high-memory class to moderate-memory by changing from hash join to merge join.
On a per-instance basis you could set HASH_JOIN_ENABLED to false, and set it to true only on a per-session basis. Then you can let the optimizer choose sort merge join more often (as opposed to telling the optimizer never to use hash joins). If you need to support thousands of users, you must create access paths such that queries do not touch much data. These monitors often give you a better idea of CPU utilization and system performance than monitoring the execution time of a query. They show concretely how you might adjust your system workload so as to accommodate the necessary number of processes and users.
Thus you can support many more users by having them run serially, or by having them run with less parallelism. Thus the amount of physical memory on the machine imposes another limit on total number of parallel queries you can run involving hash joins and sorts.
You could not allow everyone to run hash joins, even though they outperform sort merge joins--because you do not have the memory to support this level of workload.
You decide to leave such tasks as index retrievals and small sorts out of the picture, concentrating on the high memory processes.

Less than 5MB of hash area would be available for each process, whereas 8 MB is the recommended minimum.
But if you want 32 MB available for lots of hash joins, the system could only support 2 or 3 users. Sort merge joins require less memory, but throughput will go down because they are not as efficient as hash joins.
Instead of all users doing sorts with a small sort area, you could have a few users doing high-memory hash joins, and most users doing low-memory index joins and using summary tables.
A high transaction rate (more than 2 or 3 per minute) on the ST enqueue may result in poor scalability on OPS systems with many nodes, or a timeout waiting for space management resources.
On a parallel server a certain number of parallel cache management (PCM) locks are assigned to each data file.
This is a good practice which ensures that data dictionary activity such as space management never interferes with the data tablespaces at a cache management level (error 1575). At runtime, a parallel query will be executed sequentially if insufficient query servers are available.
For example, if there are 10 CPUs and 5 query servers, the operating system distributes the 5 processes among the CPUs.
Disk affinity exploits a "shared nothing" architecture by minimizing data shipping and internode communication.
As a result, we have even less disk operations but now, even a MySQL crash may cause data loss.
At first innodb_flush_log_at_trx_commit = 2 was used, then innodb_flush_log_at_trx_commit = 1 and finally innodb_flush_log_at_trx_commit = 0.
InnoDB performs writes in a sequential manner, starting from the first byte of the first file and ending at the last byte in the last file.
If we modify the data that’s already stored in the buffer pool, such modifications are applied and the relevant pages are marked as dirty.
It’s true also in the cloud - you may have an option to “buy” disk volume with some number of IOPS but at the end you have to check what’s the maximum level of performance you can get from it under your exact workload.
It’s not only flushing but also writes to InnoDB redo log, double-write buffer and some other internal structures. This should be enough data to benefit from write aggregation when redo log is flushed to the tablespaces. We also have to take under the consideration spikyness of the workload - if there are periods of increased writes, we need to do sampling to cover them. The second half provides detailed information to help you diagnose and solve tuning problems.
Since hash joins and large sorts are memory hungry operations, you may want to configure fewer processes, each with a greater limit on the amount of memory it can use. If you want to change the size of the SGA you must shut down the database, make the change, and restart the database. A value of 100 causes the optimizer to choose a parallel plan unless a serial plan would complete faster. Should this happen, Oracle will execute the query sequentially, or give an error if PARALLEL_MIN_PERCENT is set. However, if servers are continuously starting and shutting down, you should consider increasing the value of the parameter PARALLEL_MIN_SERVERS.
For this example, assume you will have two concurrent queries with 20 as the average degree of parallelism. Thus you should normally have 4 buffers in shared memory: 2 in the local shared pool and 2 in the remote shared pool.
This can dramatically increase the performance of sort and hash operations since the entire operation is more likely to be performed in memory. Thus you can get a list of all employees who are not in the Shipping or Receiving departments. Statistics and index information from the partition tables are combined and used by the optimizer just as if the view were a real table. Avoiding the buffer cache can thus provide performance improvement by a factor of 3 or more. In addition, this feature may require operating system specific configuration and may not be supported on all platforms.
If all objects are striped over all disks, then loss of any disk takes down the entire database. Furthermore, on parallel server systems, those devices should be spread over multiple nodes. The 10 processes will load the first partition in parallel on the first 10 disks, then the second partition in parallel on the second 10 disks, and so on through the twelfth partition. If you allocate a large extent but only need to use a small amount of space, the unused space in the extent is tied up. Because indexes are accessed much more randomly than tables and temporary space, OS striping with a small stripe width is often the best choice. However, many different objects (such as all partitions of a partition view) can be analyzed in parallel. A DBA or application designer should know which columns are the most frequently used in predicates.
Use your operating system monitor to check wait time: The sum of time waiting and time running equals 100%. Queries at the head of the queue would have a fast response time, those at the end of the queue would have a slow response time. You can use initialization parameters to limit available memory and thus force the optimizer to stay within certain bounds. Conversely, you could set HASH_JOIN_ENABLED to true on a per-instance basis, and make it false for particular sessions.
In this way, hash join can still be used for small tables: the optimizer has a memory budget within which it can make decisions about which join method to use. Consult your operating system documentation to determine whether your system supports graphical system monitors. This configuration can support 17 parallel queries, or 170 serial queries, but response times may be significantly higher than if you were using hash joins. You might have 300 processes, of which 200 must come from the parallel pool and 100 are single threaded.
If you must have 300 processes, you may need to force them to use other join methods in order to change them from the highly memory intensive class to the moderately memory intensive class. By contrast, if users are just computing aggregates the system needs adequate sort area size--and can have many more users.
Since this figure is at the low end of the medium weight query class, you must rule out parallel query operations, which use even more resources.
Do not accept the default value of 40K for next extent size, because this will result in many requests for space per second.
This has advantages over default DBA locking because with the default, you would need to acquire a million locks in order to read 1 million blocks.
PARALLEL_MIN_PERCENT sets the minimum percentage of the target number of query servers which must be available, if the query is to run in parallel. Thus, with 10 nodes and 2 users, the parallel query feature will run query 1 on the first 5 nodes and query 2 on the second 5 nodes. By default (innodb_flush_log_at_trx_commit = 1), data is flushed to both log buffer in memory and redo log on disk at the transaction commit.
As you can see, the difference between the most durable mode and the less safe ones is significant. After it reaches that place, the next write will hit the first byte of the first file and the whole process repeats. If not all of the needed pages are in the memory, those missing pages will be read from disk and, again, marked as dirty in the buffer pool. That’s why changes are written in a sequential manner to the redo log and random writes hit the memory first (buffer pool). It simply means that frequently used pages stay in the buffer pool and least frequently used pages are removed from it. We have innodb_buffer_pool_pages_data which tells us how many pages we used in the buffer pool.
Note that you can change some of these parameters dynamically with ALTER SYSTEM or ALTER SESSION statements.
In this case response time of a parallel plan will be higher and total system resource use would be much greater than if it were done by a serial plan using an index. However, if memory is a concern for your system, you may want to limit the amount of memory allocated for sorts and hashing operations and increase the size of the buffer cache so that data blocks from temporary sort segments can be cached in the buffer cache. If the sort area size is very small, there will be many runs to merge, and it may take multiple passes to merge them together. Similarly, temporary tablespaces, introduced in release 7.3, improve efficiency of sort and hash joins. By contrast, CREATE INDEX and CREATE TABLE AS SELECT statements do not use the buffer cache. Thirty 4Gb disks will be used for base table data, 10 disks for index, and 30 disks for temporary space.
These recommended sizes represent a compromise between the requirements of query performance, backup and restore performance, and load balancing. Furthermore, all database files may have to be restored from backups, even if each file has only a small fraction actually stored on the failed disk.
Even cheaper than mirroring is RAID technology, which avoids full duplication in favor of more expensive write operations.
Warehouse operations are typically CPU bound; thus the default is a good choice, especially if you are using the new asynchronous readahead feature. Oracle initializes each block in the datafile, so we want to add the datafiles in parallel to speed up datafile initialization.
If you lose a disk in an OS striped temporary space, you will probably have to drop and recreate the tablespace. Because of the performance advantage of the UNRECOVERABLE option, consider mirroring or recreation in the event of media failure. You can take this requirement even lower by using the multi-threaded server, and support even more users. In a parallel query, since each query server process does at most 1 hash join at a time, you would need 1 hash area size per server. Queueing jobs is thus another way to reduce the number of processes but not reduce parallelism; its disadvantage, however, is a certain amount of administrative overhead.
Decrease the demand for GROUP BY sorting by creating summary tables and encouraging users and applications to reference summaries rather than detailed data.
One quarter of the total 2G of memory might be used by the SGA, leaving 1.5 G of memory to handle all the processes. During a full table scan a PCM lock must then be acquired for each block read into the scan.
When PARALLEL_MIN_PERCENT is set to n, an error message will be sent if n percent query server processes are not available. Writing without a flush does not force the disk write - data can be (and in fact, it is) stored in the operating system’s disk buffers and flushed to disk later. Then write aggregation is performed and data is pushed to tablespaces in the most efficient way possible - as sequential as it’s doable under current workload.
If such a page is a ‘dirty’ page (contains modifications), it needs to be flushed to disk before it can be discarded.
We have also innodb_buffer_pool_pages_free which tells us how many free pages we have.
It comes out of process memory: and both the size of a process' memory and the number of processes can vary greatly. Additional disks are needed for rollback segments, control files, log files, possible staging area for loader flat files, and so on. Setting the stripe size too small will detract from performance, particularly for backup and restore operations. Consider explicitly setting the parallel degree to 2 * CPUs if you are performing synchronous reads. Thus a grouping of !10 would mean that you would only have to acquire one tenth as many PCM locks as with the default. If no parallel query processes are available, a parallel query will be executed sequentially. This change is crucial - we don’t have a flush per transaction commit but a flush per second.
Once data is flushed, it can be removed from both the InnoDB buffer pool and the InnoDB redo log.
The parallel plan could use up to D times more resource, where D is the degree of parallelism.
Consider reducing parallelism for objects that are frequently accessed by two or more concurrent parallel queries. The default storage parameters can be customized for each object created in the tablespace, if needed. By reducing the memory of each query server by a factor of 2, and reducing the parallelism of a single query by a factor 2, the system can accommodate 2*2 = 4 times more concurrent queries. Such a small hash area size is likely to be ineffective, so you may opt to disable hash joins altogether. It brings some danger too - if the whole instance went down between the moment of committing and the moment of data flush, those transactions may get lost. Another status counter that we have is innodb_buffer_pool_pages_dirty which tells us how many dirty buffer pool pages are stored in memory and eventually will have to be flushed. A value between 0 and 100 sets an intermediate trade-off between throughput and response time. Each partition will be spread evenly over 10 disks, so that a scan which accesses few partitions, or a single partition, can still proceed with full parallelism.
As a rule of thumb, performance with a grouping of !10 might be comparable to the speed of hashed locking.
Finally, there’s a configuration variable, innodb_max_dirty_pages_pct, which defines how many dirty pages we can have compared to the buffer pool size before InnoDB starts to flush them more aggressively.

Increase attachment size exchange 2010 mac book
Increase font size in mac menu bar missing
Male enlargement breast
Top 5 male enhancement supplements work

Comments to “How to check buffer pool size in oracle”

  1. ANILSE writes:
    Strive, however watch out in doing the train program, in the event nutritional vitamins , minerals, herbs enlargement.
  2. BARIS writes:
    Actual methods to enlarge their members, with out risk, without losing you most.
  3. Arzu_18 writes:
    Yourself this favor: avoid non-surgical.