Dockerfile Download Free Tar Gz And Extract
Robinette Stiles
I am new to docker and creating a Dockerfile for a program that I would like to run in docker. I have verified it runs under the alpine Linux. The program comes in a tgz file and for purposes, it's called example.tgz. This file has the folder structure etc, lib, and usr folders and each folder has sub folders. So this extracts to the root of linux and places the files in the right locations of etc, lib, and usr. How do I get the Dockerfile to extract this file so the etc, lib, and usr files are in the proper locations?
Both ADD and COPY copy files and directories from the host machine into a Docker image, the difference is that ADD can also extract and copy local tar archives and it can also download files from URLs (a.k.a. the internet), and copy them into the Docker image. The best practice is to use COPY.
If you are running the Agent as a binary on a host, configure your tag extractions with the Agent tab instructions. If you are running the Agent as a container, configure your tag extraction with the Containerized Agent tab instructions.
The exec form of the Dockerfile ENTRYPOINT is used so that there is no shell wrapping the Java process. The advantage is that the java process responds to KILL signals sent to the container. In practice, that means (for instance) that, if you docker run your image locally, you can stop it with CTRL-C. If the command line gets a bit long, you can extract it out into a shell script and COPY it into the image before you run it. The following example shows how to do so:
The extract mode feature is still in a very experimental stage and may not work as intended. The Semgrep team is planning to improve this feature in the future. Reach out for help and suggestions on the Semgrep Community Slack.
However, with extract mode, you can provide Semgrep with instructions on how to extract any Bash commands used in a Docker RUN instruction or as an argument to Python's os.system standard library function.
By adding the extract mode rules as shown in the previous code snippet, Semgrep matches Bash code contained in the following Python file and reports the contained Bash as matching against the curl-eval rule.
The extract key is required in extract mode. The value must be a metavariable appearing in your pattern(s). Semgrep uses the code bound to the metavariable for subsequent queries of non-extract mode rules targeting dest-language.
Extract the matched content as a JSON array. Each element of the array correspond to a line the resulting source code. This value is useful in extracting code from JSON formats such as Jupyter Notebooks.
While extract mode can help to enable rules which try and track taint across a language boundary within a file, taint rules cannot have a source and sink split across the original file and extracted text.
We recommend setting up a DSN for the database if you're running the extractor against an ODBC source on Windows. Then, the Windows DSN system handles password storage instead of keeping it in the configuration file or as an environment variable. In addition, the connection strings will be less complex.
Use a Docker container to run the DB extractor on Mac OS or Linux systems. This article describes how to create file-specific bindings with the container. You should consider sharing a folder if a deployment uses more than one file, for example, logs and local state files.
If the database table has a column containing an incremental field, you can set up the extractor to only process new or updated rows since the last extraction. An incremental field is a field that increases for new entries, such as time stamps for the latest update or insertions if the rows never change, or a numerical index.
If 10 or more rows have the same time, the start-at field won't be updated since the largest value of the incremental field didn't increase in the run. The next run will start with the same value, and the extractor is stuck in a loop. However, changing the query from >= to > can cause data loss if many rows have the same value for the incremental field.
There might be a slight boost in the extractor runtime if the database has an index on the incremental field. However, for a standard run of the DB extractor, network delays are often the main bottleneck for performance, so usually, the difference is minimal.
The key phrase extraction container image can be found on the mcr.microsoft.com container registry syndicate. It resides within the azure-cognitive-services/textanalytics/ repository and is named keyphrase. The fully qualified container image name is, mcr.microsoft.com/azure-cognitive-services/textanalytics/keyphrase.
In this docker file, you can see that we are doing a couple of stuff, first, we extract the jar layers in a docker image called builder, then we copy the layers in a different image, every copy command will create a different layer in our image, based on this docker image layers docker will know if where are some changes in there and based on that push only the changes related to some specific layer
This step uses docker/metadata-action to extract tags and labels that will be applied to the specified image. The id "meta" allows the output of this step to be referenced in a subsequent step. The images value provides the base name for the tags and labels.
The above workflow is triggered by a push to the "release" branch. It checks out the GitHub repository, and uses the login-action to log in to the Container registry. It then extracts labels and tags for the Docker image. Finally, it uses the build-push-action action to build the image and publish it on the Container registry.
ADD lets you do that too, but it also supports 2 other sources. First, you can use a URL instead of a local file / directory. Secondly, you can extract a tar file from the source directly into the destination.
A malicious Docker container started running in the environment. The container was extracted to analyze it in controlled conditions and examine its contents to discover indicators of compromise.
The OVA is an Open Virtual Appliance (i.e. a virtual machine with all the disks and settings in one convenient package). I'd never had a need to tear one down before, but it turns out that its nothing but a tar file. Assuming you had a MyVirtualMachine.ova, to extract its as simple as:
At a minimum you should end up with an Open Virtualization Format (OVF) file and a Virtual Machine Dist (VMDK) file. At this point you could use any number of hypervisors to mount the vmdk and possibly extract the data that way. I don't like this because booting a kernel will populate a number of paths, devices, and special files that I don't want to clutter my docker image.
Instead of using a hypervisor, I used a tool called, qemu-img. This tool has the capability to convert between disk types like qcow2, vbox, and vmdk. The conversion that I really wanted though was a raw format. This converts the virtual disk image into a format that looks like it was extracted directly from /dev/sda with dd (i.e. a raw dump of the hard disk). Note: This can be disk space intensive because the modern virtualized disk images are compressed while the raw dumps are not. For example, a 5 GiB vmdk could easily expand into a 64 GiB file! To convert from a vmdk (MyDisk.vmdk) to a raw dump file (MyDisk.raw), run the following:
While it is possible to convert a Spring Boot uber jar into a docker image with just a few lines in the Dockerfile, we will use the layering feature to create an optimized docker image.When you create a jar containing the layers index file, the spring-boot-jarmode-layertools jar will be added as a dependency to your jar.With this jar on the classpath, you can launch your application in a special mode which allows the bootstrap code to run something entirely different from your application, for example, something that extracts the layers.
Next step is to extract those layers to create an optimized docker image. Hopefully, the -Djarmode=layertools system property exists with extract command in aim to extract layers from the jar file and unpack it into seperate directories.
A Docker image is composed of a stack of layers each representing an instruction in our Dockerfile, so we can use those extracted layers from fat Jar to construct Docker layers that change the least.
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