This process may take a long time if the file is large. SQL Server uses the instant file initialization feature for data files.

If the data file is a log file, or if instant file initialization is not enabled, SQL Server performs zero stamping. You should switch the value of the StampFiles parameter during testing to make sure that both instant file initialization and zero stamping are operating correctly.

It can also include a secondary stream name and type. For example, the FileName parameter may be set to file. We recommend that you perform stream tests. Size in MB of the increment by which the file grows or shrinks. For more information, see the "ShrinkUser section" part of this article. If you set the Increment parameter to 0, you set the file to be non-shrinkable. In this case, you must set the Shrinkable parameter to false. If you set the Increment parameter to a value other than 0, you set the file to be shrinkable.

In this case, you must set the Shrinkable parameter to true. We recommend that you enable both the sparse file and the streams, and then perform a test pass. The value cannot exceed the following value: CPUCount The total number of all users also cannot exceed this value. A value of 0 means that you cannot create random access users. Most of the sessions do not have active requests.

The start of the region is randomly selected. The minimum value is 0. The maximum value is limited by system memory. Optionally, you can filter your query by time or by SCN. This procedure closes all the redo log files and allows all the database and system resources allocated by LogMiner to be released. If this procedure is not executed, LogMiner retains all its allocated resources until the end of the Oracle session in which it was invoked.

This section provides several examples of using LogMiner in each of the following general categories:. However, setting the parameter explicitly lets you predict the date format.

The following examples demonstrate how to use LogMiner when you know which redo log files contain the data of interest. This section contains the following list of examples; these examples are best read sequentially, because each example builds on the example or examples that precede it:. The easiest way to examine the modification history of a database is to mine at the source database and use the online catalog to translate the redo log files.

This example shows how to do the simplest analysis using LogMiner. This example finds all modifications that are contained in the last archived redo log generated by the database assuming that the database is not an Oracle Real Application Clusters database. This example assumes that you know that you want to mine the redo log file that was most recently archived.

Specify the redo log file that was returned by the query in Step 1. The list will consist of one redo log file. Note that there are four transactions two of them were committed within the redo log file being analyzed, and two were not.

The output shows the DML statements in the order in which they were executed; thus transactions interleave among themselves. As shown in the first example, Example 1: Finding All Modifications in the Last Archived Redo Log File , LogMiner displays all modifications it finds in the redo log files that it analyzes by default, regardless of whether the transaction has been committed or not.

In addition, LogMiner shows modifications in the same order in which they were executed. Because DML statements that belong to the same transaction are not grouped together, visual inspection of the output can be difficult.

In this example, the latest archived redo log file will again be analyzed, but it will return only committed transactions.

Although transaction 1. In this example, therefore, transaction 1. The two transactions that did not commit within the redo log file being analyzed are not returned. However, one aspect remains that makes visual inspection difficult: the association between the column names and their respective values in an INSERT statement are not apparent.

Note that specifying this option will make some of the reconstructed SQL statements nonexecutable. This example shows how to use the dictionary that has been extracted to the redo log files. When you use the dictionary in the online catalog, you must mine the redo log files in the same database that generated them.

Using the dictionary contained in the redo log files enables you to mine redo log files in a different database. The dictionary may be contained in more than one redo log file. Therefore, you need to determine which redo log files contain the start and end of the dictionary. Find a redo log file that contains the end of the dictionary extract. This redo log file must have been created before the redo log file that you want to analyze, but should be as recent as possible.

Find the redo log file that contains the start of the data dictionary extract that matches the end of the dictionary found in the previous step:. Specify the list of the redo log files of interest. Add the redo log files that contain the start and end of the dictionary and the redo log file that you want to analyze.

You can add the redo log files in any order. In the output, LogMiner flags a missing redo log file. LogMiner lets you proceed with mining, provided that you do not specify an option that requires the missing redo log file for proper functioning. To reduce the number of rows returned by the query, exclude from the query all DML statements done in the sys or system schemas.

This query specifies a timestamp to exclude transactions that were involved in the dictionary extraction. The DDL transactions, 1. In both transactions, the DML statements done to the system tables tables owned by sys are filtered out because of the query predicate. The DML transaction, 1. The update operation in this transaction is fully translated. However, the query output also contains some untranslated reconstructed SQL statements.

Most likely, these statements were done on the oe. This includes statements executed by users and internally by Oracle. Because the dictionary may be contained in more than one redo log file, you need to determine which redo log files contain the start and end of the data dictionary.

Find a redo log that contains the end of the data dictionary extract. This redo log file must have been created before the redo log files that you want to analyze, but should be as recent as possible. Find the redo log file that contains the start of the data dictionary extract that matches the end of the dictionary found by the previous SQL statement:. To successfully apply DDL statements encountered in the redo log files, ensure that all files are included in the list of redo log files to mine.

The missing log file corresponding to sequence must be included in the list. Determine the names of the redo log files that you need to add to the list by issuing the following query:. Include the redo log files that contain the beginning and end of the dictionary, the redo log file that you want to mine, and any redo log files required to create a list without gaps. To reduce the number of rows returned, exclude from the query all DML statements done in the sys or system schemas.

The query returns all the reconstructed SQL statements correctly translated and the insert operations on the oe. Suppose you want to mine redo log files generated since a given time. The following procedure creates a list of redo log files based on a specified time. Suppose you realize that you want to mine just the redo log files generated between 3 p. However, the query predicate is evaluated on each row returned by LogMiner, and the internal mining engine does not filter rows based on the query predicate.

Although this does not change the list of redo log files, LogMiner will mine only those redo log files that fall in the time range specified. The previous set of examples explicitly specified the redo log file or files to be mined.

However, if you are mining in the same database that generated the redo log files, then you can mine the appropriate list of redo log files by just specifying the time or SCN range of interest. This section contains the following list of examples; these examples are best read in sequential order, because each example builds on the example or examples that precede it:.

This example is similar to Example 4: Using the LogMiner Dictionary in the Redo Log Files , except the list of redo log files are not specified explicitly. This example assumes that you want to use the data dictionary extracted to the redo log files. Step 1 Determine the timestamp of the redo log file that contains the start of the data dictionary. Compare the output in this step to the output in Step 2.

To reduce the number of rows returned by the query, exclude all DML statements done in the sys or system schema. This example shows how to specify an SCN range of interest and mine the redo log files that satisfy that range.

You can use LogMiner to see all committed DML statements whose effects have not yet been made permanent in the datafiles. LogMiner will add the rest of the SCN range contained in the online redo log files automatically, as needed during the query execution. Use the following query to determine whether the redo log file added is the latest archived redo log file produced. This examples assumes that you want to monitor all changes made to the table hr.

This select operation will not complete until it encounters the first redo log file record that is generated after the time range of interest 5 hours from now. The examples in this section demonstrate how to use LogMiner for typical scenarios. This section includes the following examples:.

This example shows how to see all changes made to the database in a specific time range by a single user: joedevo. Connect to the database and then take the following steps:. To use LogMiner to analyze joedevo 's data, you must either create a LogMiner dictionary file before any table definition changes are made to tables that joedevo uses or use the online catalog at LogMiner startup.

This example uses a LogMiner dictionary that has been extracted to the redo log files. Assume that joedevo has made some changes to the database. You can now specify the names of the redo log files that you want to analyze, as follows:. You decide to find all of the changes made by user joedevo to the salary table. You discover that joedevo requested two operations: he deleted his old salary and then inserted a new, higher salary. You now have the data necessary to undo this operation.

In this example, assume you manage a direct marketing database and want to determine how productive the customer contacts have been in generating revenue for a 2-week period in January. To download the entire file, you need to include the --starting-token 0 parameter. The following example saves the output to a local file named www. Server log files. Screenshot of the gimp. Setting up Azure Blob storage is outside the scope of this article, but you can find documentation here: Introduction to blob storage — Azure Storage Microsoft Docs.

Note : It is not a requirement to use Azure storage — see the note on download URL later in this post. The rest of this process will remain the same but the URL you use will have additional information encoded at the end. Screenshot showing that setting container "macapps" public access level is Private when public access disallowed. I like using Azure because it gives us more control over the process and the version that we install, but the rest of the process in this post will work fine using either Azure Blob storage or the public download URL from the Gimp servers.

In this section we will walk through an example shell script from the Intune Shell Script GitHub Repository to download and install Gimp. Open the installGimp. The bits we might want to change are shown on lines These variables control how the script will behave. The rest of the script can be left as is, but it is a good idea to read through it to ensure that you understand what it does. Now we have our script, we need to test it. The easiest way to do that is to run it on a test device.

We need to make the script executable with chmod which we will run in a terminal window. Next, we can give the script a test run to check that it works. The Gimp splash screen should appear, and the application should start. Screenshot of the GIMP splash screen. Assuming everything went well to this point, all we need to do now is to deploy the script via Intune.

The final step on the client Mac is to check that the app has installed, and we can launch it. The Gimp app should launch. Example of launching the Gimp app to validate app installation on a macOS device. All that is left is to set the assignment policy of the script to include all the users that you need to install the Gimp app to.

Luckily, the example script already handles updates so all that we need to do is to upload a newer version of gimp.

If you want more detail, when we created our script policy in Intune, we set the schedule to run every day. To prevent the script from installing Gimp every time it runs, there are a few functions to handle updates and downloads.

We can see these functions in action simply by running the script twice. On a test machine, if we download installGimp. To show the update process working, update the gimp. Repeat steps 1 and 2 above. During step 2, make sure that you use the same file name and that you check the Overwrite if files already exist checkbox.

Screenshot of the Overwrite if files already exist checkbox option in Intune. We have our Gimp script working as we want, but what about other installer files?

In this example, we are going to look at modifying the InstallGimp. Forces you to supply all parameters, where fread saves you work by automatically guessing the delimiter, whether or not the file has a header, and how many lines to skip. Are built on a different underlying infrastructure. Readr functions are designed to be quite general, which makes it easier to add support for new rectangular data formats. Joe Cheng for showing me the beauty of deterministic finite automata for parsing, and for teaching me why I should write a tokenizer.

JJ Allaire for helping me come up with a design that makes very few copies, and is easy to extend. Dirk Eddelbuettel for coming up with the name! Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. Installation The easiest way to get readr is to install the whole tidyverse: install. Alternatives There are two main alternatives to readr: base R and data.