Can i download data definition files from progress db

Now that we have a database we will need to pass in the --sql-append switch to tell cloc not to wipe out this database but instead add more data:. Now the fun begins--we have a database, code. To produce this output one must now use the switch. The biggest flaw with this approach is that gearing ratios are defined for logical lines of source code not physical lines which cloc counts. The values in cloc's 'scale' and '3rd gen. Identifying comments within source code is trickier than one might expect.

Many languages would need a complete parser to be counted correctly. The following are known problems:. If cloc does not recognize a language you are interested in counting, post the following information to a Feature Request at cloc's SourceForge page: File extensions associated with the language. If the language does not rely on file extensions and instead works with fixed file names or with!

A description of how comments are defined. Links to sample code. These examples come from his Hello World Collection.

Anton Demichev found a flaw with the JSP counter in cloc v0. Michael Bello provided code for the --opt-match-f , --opt-not-match-f , --opt-match-d , and --opt-not-match-d options. Mahboob Hussain inspired the --original-dir and --skip-uniqueness options, found a bug in the duplicate file detection logic and improved the JSP filter.

Randy Sharo found and fixed an uninitialized variable bug for shell scripts having only one line. Joel Oliveira provided code to let --exclude-list-file handle directory name exclusion. Both client and server must be For other products, if not explicitly stated in the product-specific documentation, it should be assumed that such clients cannot operate with a database server with a different time zone file than the client.

This is due to different daylight saving time DST rules in effect for the time zone regions affected between the different time zone file versions at the client and on the server.

Note if an application connects to different databases directly or via database links, it is recommended that all databases be on the same time zone file version. This is due to different DST rules in effect for the time zone regions affected between the different time zone file versions across the different database servers.

If you do not set the database time zone, then it defaults to the time zone of the server's operating system. The time zone may be set to a named region or an absolute offset from UTC.

To set the time zone to a named region, use a statement similar to the following example:. For example:. For example, '' specifies the time zone that is 7 hours behind UTC.

For example, if the UTC time is a. Time zone region name: Oracle Database returns the value in the time zone indicated by the time zone region name. An expression: If an expression returns a character string with a valid time zone format, then Oracle Database returns the input in that time zone.

Otherwise, Oracle Database returns an error. Oracle Database automatically determines whether Daylight Saving Time is in effect for a specified time zone and returns the corresponding local time. The periods when Daylight Saving Time begins or ends are boundary cases. For example, in the Eastern region of the United States, the time changes from a. The interval between and a. Values in that interval are invalid. When Daylight Saving Time ends, the time changes from a.

Values from that interval are ambiguous because they occur twice. TZR represents the time zone region in datetime input strings. Examples are ' PST ' for U. If a time zone region is associated with the datetime value, then the database server knows the Daylight Saving Time rules for the region and uses the rules in calculations. The rest of this section contains examples that use datetime data types.

This example shows the effect of adding 8 hours to the columns. Because a time zone region is associated with the datetime value for orderdate2 , the Oracle Database server uses the Daylight Saving Time rules for the region. Thus the output is the same as in Example Note: This chapter describes Oracle Database datetime and interval data types.

It does not attempt to describe ANSI data types or other kinds of data types unless noted. Interval Data Types Interval data types store time durations.

Inserting Values into Interval Data Types You can insert values into an interval column in the following ways: Insert an interval as a literal. Datetime Comparisons When you compare date and timestamp values, Oracle Database converts the data to the more precise data type before doing the comparison.

The following information is also included for each time zone: Offset from Coordinated Universal Time UTC Transition times for Daylight Saving Time Abbreviations for standard time and Daylight Saving Time Oracle Database supplies multiple versions of time zone files, and there are two types of file associated with each one: a large file, which contains all the time zones defined in the database, and a small file, which contains only the most commonly used time zones.

Upgrading the Time Zone File and Timestamp with Time Zone Data The time zone files that are supplied with the Oracle Database are updated periodically to reflect changes in transition rules for various time zone regions. Note: Oracle Database 9 i includes version 1 of the time zone files, and Oracle Database 10 g includes version 2. For Oracle Database 11 g , release 2, all time zone files from versions 1 to 14 are included. Various patches and patch sets, which are released separately for these releases, may update the time zone file version as well.

Note: For any time zone region whose transition rules have been updated, the upgrade process discussed throughout this section, "Upgrading the Time Zone File and Timestamp with Time Zone Data" , affects only timestamps that point to the future relative to the effective date of the corresponding DST rule change. Preparing to Upgrade the Time Zone File and Timestamp with Time Zone Data Before you actually upgrade any data, you should verify what the impact of the upgrade is likely to be.

Note: Note that only one DBA should run the prepare window at one time. Also, make sure to correct all errors before running the upgrade. Shut down the database. In Oracle RAC, you must shut down all instances.

Restart the database in normal mode. Note: Tables containing timestamp with time zone columns need to be in a state where they can be updated. So, as an example, the columns cannot have validated and disabled check constraints as this prevents updating. As an example, first, prepare the window. Oracle recommends that you set the database time zone to UTC to avoid data conversion and improve performance when data is transferred among databases.

This is especially important for distributed databases, replication, and exporting and importing. SYSDATE returns the date and time of the operating system on which the database resides, taking into account the time zone of the database server's operating system that was in effect when the database was started.

Support for Daylight Saving Time Oracle Database automatically determines whether Daylight Saving Time is in effect for a specified time zone and returns the corresponding local time. Verify that the hint provides better results. Compute statistics. If you do not analyze often and you can spare the time, it is a good practice to compute statistics. This is particularly important if you are performing many joins, and it will result in better plans.

Alternatively, you can estimate statistics. If you use different sample sizes, the plan may change. Generally, the higher the sample size, the better the plan. With a large query referencing five or six tables, it may be difficult to determine which part of the query is taking the most time. You can isolate bottlenecks in the query by breaking it into steps and analyzing each step. If the cause of regression cannot be traced to problems in the plan, the problem must be an execution issue.

For data warehousing operations, both serial and parallel, consider how the plan uses memory. Check the paging rate and make sure the system is using memory as effectively as possible. Check buffer, sort, and hash area sizing. If you are using parallel execution, is there unevenness in workload distribution? For example, if there are 10 CPUs and a single user, you can see whether the workload is evenly distributed across CPUs. This is a good indication of skew and does not require single user operation.

Concurrently running tasks make it harder to see what is going on, however. If parallel execution problems occur, check to be sure you have followed the recommendation to spread data over at least as many devices as CPUs. If so, consider increasing parallelism up to the number of devices. Is the system CPU-bound with too much parallelism?

Check the operating system CPU monitor to see whether a lot of time is being spent in system calls. The resource might be overcommitted, and too much parallelism might cause processes to compete with themselves. After your system has run for a few days, monitor parallel execution performance statistics to determine whether your parallel processing is optimal. Do this using any of the views discussed in this section.

In Oracle Real Application Clusters, global versions of the views described in this section aggregate statistics from multiple instances.

You can consult this view to reconfigure SGA size in response to insufficient memory problems for parallel queries. It also displays real-time data about the processes working on behalf of parallel execution. Thus, all session statistics available to a normal session are available for all sessions performed using parallel execution.

You might need to adjust some parameter settings to improve performance after reviewing data from these views. Query these views periodically to monitor the progress of long-running parallel operations. Using a ratio analysis, you can determine the percentage of the total tablespace activity used by each file in the tablespace.

If you make a practice of putting just one large, heavily accessed object in a tablespace, you can use this technique to identify objects that have a poor physical layout.

Ensure that space is allocated evenly from all files in the tablespace. As a simple example, consider a hash join between two tables, with a join on a column with only two distinct values. At best, this hash function will have one hash value to parallel execution server A and the other to parallel execution server B. A DOP of two is fine, but, if it is four, then at least two parallel execution servers have no work.

To discover this type of skew, use a query similar to the following example:. The best way to resolve this problem might be to choose a different join method; a nested loop join might be the best option. Now, assume that you have a join key with high cardinality, but one of the values contains most of the data, for example, lava lamp sales by year.

The only year that had big sales was , and thus, the parallel execution server for the records will be overwhelmed. You should use the same corrective actions as described previously. A table queue is the pipeline between query server groups, between the parallel coordinator and a query server group, or between a query server group and the coordinator.

A table queue connecting 10 consumer processes to 10 producer processes has 20 rows in the view. Compare this with the optimizer estimates; large variations might indicate a need to analyze the data using a larger sample. Large variances indicate workload imbalances. You should investigate large variances to determine whether the producers start out with unequal distributions of data, or whether the distribution itself is skewed.

If the data itself is skewed, this might indicate a low cardinality, or low number of distinct values. The statistics include total number of queries, DML and DDL statements executed in a session and the total number of intrainstance and interinstance messages exchanged during parallel execution during the session.

These examples use the dynamic performance views described in "Monitoring Parallel Execution Performance with Dynamic Performance Views". In this example, sessions 9 is the query coordinator, while sessions 7 and 21 are in the first group, first set. Sessions 18 and 20 are in the first group, second set. The requested and granted DOP for this query is 2, as shown by Oracle's response to the following query:. The next example shows the execution of a join query to determine the progress of these processes in terms of physical reads.

Use this query to track any specific statistic:. Repeat this query as often as required to observe the progress of the query server processes. In addition, statistics also count the number of query operations for which the DOP was reduced, or downgraded, due to either the adaptive multiuser algorithm or the depletion of available parallel execution servers.

Finally, statistics in these views also count the number of messages sent on behalf of parallel execution. The following syntax is an example of how to display these statistics:. There is considerable overlap between information available in Oracle and information available though operating system utilities such as sar and vmstat on UNIX-based systems.

However, some operating systems have good visualization tools and efficient means of collecting the data.

Operating system information about CPU and memory usage is very important for assessing performance. Probably the most important statistic is CPU usage. Operating system memory and paging information is valuable for fine tuning the many system parameters that control how memory is divided among memory-intensive data warehouse subsystems like parallel communication, sort, and hash join.

In a shared-disk cluster or MPP configuration, an instance of the Oracle Real Application Clusters is said to have affinity for a device if the device is directly accessed from the processors on which the instance is running. Similarly, an instance has affinity for a file if it has affinity for the devices on which the file is stored. Determination of affinity may involve arbitrary determinations for files that are striped across multiple devices.

Somewhat arbitrarily, an instance is said to have affinity for a tablespace or a partition of a table or index within a tablespace if the instance has affinity for the first file in the tablespace. Oracle considers affinity when allocating work to parallel execution servers. The use of affinity for parallel execution of SQL statements is transparent to users.

Affinity in parallel queries increases the speed of scanning data from disk by doing the scans on a processor that is near the data. This can provide a substantial performance increase for machines that do not naturally support shared disks.

The most common use of affinity is for a table or index partition to be stored in one file on one device. This configuration provides the highest availability by limiting the damage done by a device failure and makes the best use of partition-parallel index scans.

DSS customers might prefer to stripe table partitions over multiple devices probably a subset of the total number of devices. This configuration enables some queries to prune the total amount of data being accessed using partitioning criteria and still obtain parallelism through rowid-range parallel table partition scans. If the devices are configured as a RAID, availability can still be very good.

Even when used for DSS, indexes should probably be partitioned on individual devices. Other configurations for example, multiple partitions in one file striped over multiple devices will yield correct query results, but you may need to use hints or explicitly set object attributes to select the correct DOP. For parallel DML inserts, updates, and deletes , affinity enhancements improve cache performance by routing the DML operation to the node that has affinity for the partition.

Affinity determines how to distribute the work among the set of instances or parallel execution servers to perform the DML operation in parallel. Affinity can improve performance of queries in several ways:. For certain MPP architectures, Oracle uses device-to-node affinity information to determine on which nodes to spawn parallel execution servers parallel process allocation and which work granules rowid ranges or partitions to send to particular nodes work assignment.

This reduces the chances of having multiple parallel execution servers accessing the same device simultaneously. This process-to-device affinity information is also used in implementing stealing between processes. For partitioned tables and indexes, partition-to-node affinity information determines process allocation and work assignment. For shared-nothing MPP systems, Oracle Real Application Clusters tries to assign partitions to instances, taking the disk affinity of the partitions into account.

For shared-disk MPP and cluster systems, partitions are assigned to instances in a round-robin manner. Affinity information which persists across statements improves buffer cache hit ratios and reduces block pings between instances. This section contains some ideas for improving performance in a parallel execution environment and includes the following topics:.

With the exception of parallel update and delete, parallel operations do not generally benefit from larger buffer cache sizes. Other parallel operations can benefit only if you increase the size of the buffer pool and thereby accommodate the inner table or index for a nested loop join. If it is memory-bound, or if several concurrent parallel operations are running, you might want to decrease the default DOP. This override occurs regardless of the default DOP indicated by the number of CPUs, instances, and devices storing that table.

The most important issue for parallel execution is ensuring that all parts of the query plan that process a substantial amount of data execute in parallel. Any other keyword or null indicates serial execution and a possible bottleneck. You can also use the utlxplp. You can increase the optimizer's ability to generate parallel plans converting subqueries, especially correlated subqueries, into joins. Oracle can parallelize joins more efficiently than subqueries.

This also applies to updates. See "Updating the Table in Parallel" for more information. Oracle cannot return results to a user process in parallel. If a query returns a large number of rows, execution of the query might indeed be faster. However, the user process can only receive the rows serially.

At a later time, users can view the result set serially. You can take advantage of intermediate tables using the following techniques:. Common subqueries can be computed once and referenced many times. This can allow some queries against star schemas in particular, queries without selective WHERE -clause predicates to be better parallelized. Note that star queries with selective WHERE -clause predicates using the star-transformation technique can be effectively parallelized automatically without any modification to the SQL.

Decompose complex queries into simpler steps in order to provide application-level checkpoint or restart. For example, a complex multitable join on a database 1 terabyte in size could run for dozens of hours. A failure during this query would mean starting over from the beginning.

If a system failure occurs, the query can be restarted from the last completed step. Implement manual parallel deletes efficiently by creating a new table that omits the unwanted rows from the original table, and then dropping the original table.

Alternatively, you can use the convenient parallel delete feature, which directly deletes rows from the original table. Create summary tables for efficient multidimensional drill-down analysis.

For example, a summary table might store the sum of revenue grouped by month, brand, region, and salesman. Reorganize tables, eliminating chained rows, compressing free space, and so on, by copying the old table to a new table. Also consider creating indexes. To avoid fragmentation in allocating space, the number of files in a tablespace should be a multiple of the number of CPUs. For optimal space management performance, you should use locally managed temporary tablespaces.

The following is an example:. When using a locally managed temporary tablespace, extents are all the same size because this helps avoid fragmentation. As a general rule, temporary extents should be smaller than permanent extents because there are more demands for temporary space, and parallel processes or other operations running concurrently must share the temporary tablespace.

Normally, temporary extents should be in the range of 1MB to 10MB. Once you allocate an extent, it is available for the duration of an operation. If you allocate a large extent but only need to use a small amount of space, the unused space in the extent is unavailable. At the same time, temporary extents should be large enough that processes do not have to wait for space. Temporary tablespaces use less overhead than permanent tablespaces when allocating and freeing a new extent.

However, obtaining a new temporary extent still requires the overhead of acquiring a latch and searching through the SGA structures, as well as SGA space consumption for the sort extent pool. See Oracle Database Performance Tuning Guide for information regarding locally-managed temporary tablespaces. After analyzing your tables and indexes, you should see performance improvements based on the DOP used.

After you understand how simple scans work, add aggregation, joins, and other operations that reflect individual aspects of the overall workload.

In particular, you should look for bottlenecks. There are several ways to optimize the parallel execution of join statements. Verify optimizer selectivity estimates. If the optimizer thinks that only one row will be produced from a query, it tends to favor using a nested loop. This could be an indication that the tables are not analyzed or that the optimizer has made an incorrect estimate about the correlation of multiple predicates on the same table. A hint may be required to force the optimizer to use another join method.

Consequently, if the plan says only one row is produced from any particular stage and this is incorrect, consider hints or gather statistics. Use hash join on low cardinality join keys. If a join key has few distinct values, then a hash join may not be optimal. If the number of distinct values is less than the DOP, then some parallel query servers may be unable to work on the particular query.

Consider data skew. If a join key involves excessive data skew, a hash join may require some parallel query servers to work more than others.

When you want to refresh your data warehouse database using parallel insert, update, or delete on a data warehouse, there are additional issues to consider when designing the physical database. These considerations do not affect parallel execution operations. These issues are:. If a parallel restriction is violated, the operation is simply performed serially.

No error message is returned. For tables created prior to Oracle9 i Database release version 9. To see what tables do not have this property, issue the following statement:. Parallel updates and deletes work only on partitioned tables. Local striping also increases availability in the event of one disk failing.

If you have global indexes, a global index segment and global index blocks are shared by server processes of the same parallel DML statement. Even if the operations are not performed against the same row, the server processes can share the same index blocks.

Each server transaction needs one transaction entry in the index block header before it can make changes to a block. There is a limitation on the available number of transaction free lists for segments in dictionary-managed tablespaces. Once a segment has been created, the number of process and transaction free lists is fixed and cannot be altered. If you specify a large number of process free lists in the segment header, you might find that this limits the number of transaction free lists that are available.

You can abate this limitation the next time you re-create the segment header by decreasing the number of process free lists; this leaves more room for transaction free lists in the segment header. The parallel DML DOP is thus effectively limited by the smallest number of transaction free lists available on the table and on any of the global indexes the DML statement must maintain. For example, if the table has 25 transaction free lists and the table has two global indexes, one with 50 transaction free lists and one with 30 transaction free lists, the DOP is limited to If the table had had 40 transaction free lists, the DOP would have been limited to By default, no process free lists are created.

The default number of transaction free lists depends on the block size. For example, if the number of process free lists is not set explicitly, a 4 KB block has about 80 transaction free lists by default. The minimum number of transaction free lists is A single ARCH process to archive these redo logs might not be able to keep up.

To avoid this problem, you can spawn multiple archiver processes. This can be done manually or by using a job queue. Parallel DML operations dirty a large number of data, index, and undo blocks in the buffer cache during a short period of time. In this case, you should consider increasing the DBW n processes. If there are no waits for free buffers, the query will not return any rows. However, after a NOLOGGING operation against a table, partition, or index, if a media failure occurs before a backup is taken, then all tables, partitions, and indexes that have been modified might be corrupted.

This alternate keyword might not be supported, however, in future releases. With this option, all objects will be created in whichever tablespace is the default during restore. Do not dump the contents of unlogged tables. This option has no effect on whether or not the table definitions schema are dumped; it only suppresses dumping the table data.

Data in unlogged tables is always excluded when dumping from a standby server. This option is not valid unless --inserts , --column-inserts or --rows-per-insert is also specified. Force quoting of all identifiers. This sometimes results in compatibility issues when dealing with servers of other versions that may have slightly different sets of reserved words.

Using --quote-all-identifiers prevents such issues, at the price of a harder-to-read dump script. The value specified must be a number greater than zero. Only dump the named section. The section name can be pre-data , data , or post-data. This option can be specified more than once to select multiple sections. The default is to dump all sections. The data section contains actual table data, large-object contents, and sequence values. Post-data items include definitions of indexes, triggers, rules, and constraints other than validated check constraints.

Pre-data items include all other data definition items. See Chapter 13 for more information about transaction isolation and concurrency control. This option is not beneficial for a dump which is intended only for disaster recovery. It could be useful for a dump used to load a copy of the database for reporting or other read-only load sharing while the original database continues to be updated. Without it the dump may reflect a state which is not consistent with any serial execution of the transactions eventually committed.

For example, if batch processing techniques are used, a batch may show as closed in the dump without all of the items which are in the batch appearing. If read-write transactions are active, the start of the dump may be delayed for an indeterminate length of time. Once running, performance with or without the switch is the same. Use the specified synchronized snapshot when making a dump of the database see Table 9. This option is useful when needing to synchronize the dump with a logical replication slot see Chapter 48 or with a concurrent session.

In the case of a parallel dump, the snapshot name defined by this option is used rather than taking a new snapshot. An exclude pattern failing to match any objects is not considered an error. This makes the dump more standards-compatible, but depending on the history of the objects in the dump, might not restore properly. Specifies the name of the database to connect to.

This is equivalent to specifying dbname as the first non-option argument on the command line. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket.

Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Never issue a password prompt. Option Use larger cluster size for example, 48 cores to run your data flow pipelines.

You can learn more about cluster size through this document: Cluster size. Option Repartition your input data. For the task running on the data flow spark cluster, one partition is one task and runs on one node. If data in one partition is too large, the related task running on the node needs to consume more memory than the node itself, which causes failure. So you can use repartition to avoid data skew, and ensure that data size in each partition is average while the memory consumption is not too heavy.

You need to evaluate the data size or the partition number of input data, then set reasonable partition number under "Optimize". For example, the cluster that you use in the data flow pipeline execution is 8 cores and the memory of each core is 20GB, but the input data is GB with 10 partitions.

For example, try to copy all files in one container, and don't use the wildcard pattern. For more detailed information, reference Mapping data flows performance and tuning guide.