Looking for:
Microsoft project 2016 lesson 1 free - |
- Microsoft project 2016 lesson 1 free
Online Course: Microsoft Project
February 12th, 5 0. Git is a distributed version control system, so by default each Git repository has a copy of all files in the entire history. Even moderately-sized teams can create thousands of commits adding hundreds of megabytes to the repository every month. As your repository grows, Git may struggle to manage all that data. Time spent waiting for git status to report modified files or git fetch to get the latest data is time wasted.
As these commands get slower, developers stop waiting and start switching context. Context switches harm developer productivity. VFS for Git uses a virtualized filesystem to bypass many assumptions about repository size, enabling the Windows developers to use Git at a scale previously thought impossible.
While supporting VFS for Git, we identified performance bottlenecks using a custom trace system and collecting user feedback. We made several contributions to the Git client, including the commit-graph file and improvements to git push and sparse-checkout.
Building on these contributions and many other recent improvements to Git, we began a project to support very large repositories without needing a virtualized filesystem. Today we are excited to announce the result of those efforts — Scalar. Scalar accelerates your Git workflow, no matter the size or shape of your repository. And it does it in ways we believe can all make their way into Git, with Scalar doing less and Git doing much more over time. Scalar is a. Scalar maximizes your Git command performance by setting recommended config values and running background maintenance.
This is how we will support the next largest Git repository: Microsoft Office. What about Linux? There is potential for porting Scalar to Linux, so please comment on this issue if you would use Scalar on Linux. Finally, I share our plan for contributing these features to the Git client.
You can get started with Scalar using the instructions below. Scalar accelerates Git commands in your existing repositories, no matter what service you use to host those repositories.
All you need to do is register your biggest repositories with Scalar and then see how much faster your Git experience becomes. To get started, download and install the latest Scalar release. Scalar currently requires a custom version of Git. We plan to remove that requirement after we contribute enough features to the core Git client.
From the working directory of your Git repository, run scalar register to make Scalar aware of your repository. By registering your repository with Scalar, it will set up some local Git config options and start running background maintenance. If you decide that you do not want Scalar running maintenance, then scalar pause will delay all maintenance for 12 hours, or scalar unregister will stop all future maintenance on the current repository.
You can watch what Scalar does by checking the log files in your. For example, here is a section of logs from my repository containing the Git source code:. These logs show the details from updating the Git commit-graph in the background, the equivalent of the scalar run commit-graph command.
You can run maintenance in the foreground using the scalar run command. When given the all option, Scalar runs all maintenance steps in a single command:. The scalar run command exists so you can run maintenance tasks on your own schedule or in conjunction with the background maintenance schedule provided by scalar register. Scalar automatically registers this new enlistment, so it will benefit from all the config options and maintenance described above. By following the snippet below, you can clone a mirror of the Scalar source code using the GVFS protocol:.
Note that this repository is not large enough to really need the GVFS protocol. We have not set up a GVFS cache server for this repository, but any sufficiently large repository being used by a large group of users should set up a co-located cache server for handling GVFS protocol requests. If you do not have the resources to set up this infrastructure, then perhaps the GVFS protocol is not a good fit, and instead you could use scalar register on an existing Git repository using the Git protocol.
When using scalar clone , the working directory contains only the files at root using the Git sparse-checkout feature in cone mode. You can expand the files in your working directory using the git sparse-checkout set command, or fully populate your working directory by running git sparse-checkout disable.
Note that the clone created the scalar directory and created the working directory is inside a src directory one level down.
This allows creating sibling directories for build output files, preventing over-taxing the work Git needs to do when managing your repository. This leads to the first big lesson we learned about making Git as fast as possible. The most common Git commands are git status to see what change are available, git add to stage those changes before committing, and git checkout to change your working directory to match a different version. We call these the core commands.
This file is read and written by each core command, presenting a minimum amount of work. Pro Tip! If you are struggling with the size of your index, then you can try running git config features. In the Windows OS repository, the index contains over three million entries.
We minimize the index file size by using an updated version of the index file format, which compresses the index file from MB to MB. Since this size primarily impacts reading and writing a stream from a single file, the average time per index entry is very low. How many paths in the index are actually in your working directory? It takes a little bit of work to design your repository to work with sparse-checkout, but it can allow most developers to populate a fraction of the total paths and still build the components necessary for their daily work.
Scalar leans into the sparse-checkout feature, so much so that the scalar clone command creates a sparse working directory by default. At the start, only the files in the root directory are present. It is up to the user to request more directories, increasing the populated size.
This mode can be overridden using the --full-clone option. The populated size is always at most the number of tracked files. The average cost of populating a file is much higher than adjusting an index entry due to the amount of data involved, so it is more critical to minimize the number of populated files than to minimize the total number of paths in the repository.
It is even more expensive to determine which populated files were modified by the user. The modified size is the number of paths in the working directory that differ from the version in the index. This includes all files that are untracked or ignored by Git. This size determines the minimum amount of work that Git must do to update the index and its caches during the core commands. Without assistance, Git needs to scan the entire working directory to find which paths were modified.
As the populated size increases, this can become extremely slow. When there are no modified files, git status takes Scalar painlessly configures your Git repository to work better with modified files using the fsmonitor Git feature and the Watchman tool. Git uses the fsmonitor hook to discover the list of paths that were modified since the last index update, then focuses its work in inspecting only those paths instead of every populated path.
Our team originally contributed the fsmonitor feature to Git , and we continue to contribute improvements. Git expects a complete copy of all objects, both currently referenced and all versions in history. This can be a massive amount of data to transfer — especially when you only need objects near your current branch do a checkout and get on with your work. This is incredibly expensive for both the server and the client. Not only is that a lot of data to transfer over the network, but the client needs to verify that all 90 million Git objects hash to the correct values.
We created the GVFS protocol to significantly reduce object transfer. This protocol is currently only available on Azure Repos. It solved one of the major issues with adapting Git to very large repositories by relaxing the distributed nature of Git to become slightly more coupled to a central server for missing objects. It has since inspired the Git partial clone feature which has very similar goals.
When using the GVFS protocol, an initial clone downloads a set of pack-files containing only commits and trees. A clone of the Windows OS repository downloads about 15 GB of data containing 40 million commits and trees. With these objects on-disk, we can generate a view of the working directory and examine commit history using git log. This pairs well with our work to reduce the populated size using sparse checkout, since reducing the populated size reduces the number of required objects.
To reduce latency and increase throughput, we allow the GVFS protocol to be proxied through a set of cache servers that are co-located with the end users and build machines. This has an added bonus of reducing stress on the central server. We intend to contribute this idea to the Git protocol. There is no free lunch. Large repositories require upkeep. Git typically handles maintenance by running garbage collection GC with the git gc --auto command at the end of several common commands, like git commit and git fetch.
Auto-GC checks your. If the thresholds are met, it completely rewrites all object data, a process that includes a CPU-intensive compression step. This can cause simple commands like git commit to be blocked for minutes. A rewrite of tens of gigabytes of data can also bring your entire system to a standstill because it consumes all the CPU and memory resources it can.
You can already disable automatic garbage collection by setting gc. However, this has the downside that your Git performance will decay slowly as you accumulate new objects through your daily work.
VFS for Git and Scalar both solve this problem by maintaining the repository in the background. This is also done incrementally to reduce the extra load on your machine.
The config step updates your Git config settings to some recommended values. The config step runs in the background so that new versions of Scalar can update the registered repositories after install.
Microsoft project 2016 lesson 1 free.Please wait while your request is being verified...
Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. In this lesson, you create a simple ETL package that extracts data from a single flat file source, transforms the data using two lookup transformations, and writes the transformed data to a copy of the FactCurrencyRate fact table in the AdventureWorksDW sample database.
As part of this lesson, you learn how to create new packages, add and configure data source and destination connections, and work with new control flow and data flow components. Before creating a package, you need to understand the formatting used in both the source data and the destination.
Then, you be ready to define the transformations necessary to map the source data to the destination. Navigate to Integration Services tutorial files.
For this tutorial, the source data is a access microsoft office 2010 of historical currency data in a flat file named SampleCurrencyData. The source data has the following four columns: the average rate of the currency, a currency key, a date key, and the end-of-day rate.
When working with flat file source data, it's important to understand how the Flat File connection manager interprets the flat file data. You probably have to change these defaults to make the string column microsoft project 2016 lesson 1 free more applicable for your data. You need to look at the data type of the destination, and then choose that type within the Flat File connection manager.
The FactCurrencyRate fact table has four columns, and has relationships to two dimension tables, as shown in the following table. Our analysis of the source and destination data formats microsoft project 2016 lesson 1 free that lookups are necessary for the CurrencyKey and Microsoft project 2016 lesson 1 free values.
The transformations that perform these lookups get those values by using the alternate keys from the DimCurrency and DimDate dimension tables.
Step 1: Create a new Integration Services project. Step 2: Add and configure a Flat File connection manager. Step 4: Add a Data Flow task to the package. Step 5: Add and configure the flat file source. Step 6: Add and configure the lookup transformations. Step 8: Annotate and format the Lesson 1 package.
Step 9: Test the Lesson 1 package. Step 1: Create a new integration services project. Skip to main content. This browser is no longer supported. Download Microsoft Edge More info. Table of contents Exit focus mode. Table of contents. In this article.
No comments:
Post a Comment