i am trying to change the source code of geth and I have some doubts.
I need to modify the following sentence in order to admit transactions with more data in them: txMaxSize = 4 * txSlotSize // 128KB
of tx_pool.go file: https://github.com/ethereum/go-ethereum/blob/master/core/tx_pool.go
I have run
git clone https://github.com/ethereum/go-ethereum.git
My doubt is whether i have to modify the file before or after running make geth
Thanks
From the perspective of building the application, you can simply:
git clone $repo
Make modifications to the file. Then
make geth
or
cd cmd/geth/
go build
to build your application. Just running go build will generally be faster for development / testing, but make geth gives more reproducible builds.
I would warn you though, that unless your plan is to make your own network with a separate client that has larger transactions, building Geth with larger transactions will just mean you can create transactions that won't propagate across the network very successfully as other nodes on the network will reject them. Most miners use Geth (or Geth forks) to assemble new blocks, and changing the parameters yourself won't change it for other nodes on the network.
Related
I have an azure blob container with data which I have not uploaded myself. The data is not locally on my computer.
Is it possible to use dvc to download the data to my computer when I haven’t uploaded the data with dvc? Is it possible with dvc import-url?
I have tried using dvc pull, but can only get it to work if I already have the data locally on the computer and have used dvc add and dvc push .
And if I do it that way, then the folders on azure are not human-readable. Is it possible to upload them in a human-readable format?
If it is not possible is there then another way to download data automatically from azure?
I'll build up on #Shcheklein's great answer - specifically on the 'external dependencies' proposal - and focus on your last question, i.e. "another way to download data automatically from Azure".
Assumptions
Let's assume the following:
We're using a DVC pipeline, specified in an existing dvc.yaml file. The first stage in the current pipeline is called prepare.
Our data is stored on some Azure blob storage container, in a folder named dataset/. This folder follows a structure of sub-folders that we'd like to keep intact.
The Azure blob storage container has been configured in our DVC environment as a DVC 'data remote', with name myazure (more info about DVC 'data remotes' here)
High-level idea
One possibility is to start the DVC pipeline by synchronizing a local dataset/ folder with the dataset/ folder on the remote container.
This can be achieved with a command-line tool called azcopy, which is available for Windows, Linux and macOS.
As recommended here, it is a good idea to add azcopy to your account or system path, so that you can call this application from any directory on your system.
The high-level idea is:
Add an initial update_dataset stage to the DVC pipeline that checks if changes have been made in the remote dataset/ directory (i.e., file additions, modifications or removals).
If changes are detected, the update_datset stage shall use the azcopy sync [src] [dst] command to apply the changes on the Azure blob storage container (the [src]) to the local dataset/ folder (the [dst])
Add a dependency between update_dataset and the subsequent DVC pipeline stage prepare, using a 'dummy' file. This file should be added to (a) the outputs of the update_dataset stage; and (b) the dependencies of the prepare stage.
Implementation
This procedure has been tested on Windows 10.
Add a simple update_dataset stage to the DVC pipeline by running:
$ dvc stage add -n update_dataset -d remote://myazure/dataset/ -o .dataset_updated azcopy sync \"https://[account].blob.core.windows.net/[container]/dataset?[sas token]\" \"dataset/\" --delete-destination=\"true\"
Notice how we specify the 'dummy' file .dataset_updated as an output of the stage.
Edit the dvc.yaml file directly to modify the command of the update_dataset stage. After the modifications, the command shall (a) create the .dataset_updated file after the azcopy command - touch .dataset_updated - and (b) pass the current date and time to the .dataset_updated file to guarantee uniqueness between different update events - echo %date%-%time% > .dataset_updated.
stages:
update_dataset:
cmd: azcopy sync "https://[account].blob.core.windows.net/[container]/dataset?[sas token]" "dataset/" --delete-destination="true" && touch .dataset_updated && echo %date%-%time% > .dataset_updated # updated command
deps:
- remote://myazure/dataset/
outs:
- .dataset_updated
...
I recommend editing the dvc.yaml file directly to modify the command, as I wasn't able to come up with a complete dvc add stage command that took care of everything in one go.
This is due to the use of multiple commands chained by &&, special characters in the Azure connection string, and the echo expression that needs to be evaluated dynamically.
To make the prepare stage depend on the .dataset_updated file, edit the dvc.yaml file directly to add the new dependency, e.g.:
stages:
prepare:
cmd: <some command>
deps:
- .dataset_updated # add new dependency here
- ... # all other dependencies
...
Finally, you can test different scenarios on your remote side - e.g., adding, modifying or deleting files - and check what happens when you run the DVC pipeline up till the prepare stage:
$ dvc repro prepare
Notes
The solution presented above is very similar to the example given in DVC's external dependencies documentation.
Instead of the az copy command, it uses azcopy sync.
The advantage of azcopy sync is that it only applies the differences between your local and remote folders, instead of 'blindly' downloading everything from the remote side when differences are detected.
This example relies on a full connection string with an SAS token, but you can probably do without it if you configure azcopy with your credentials or fetch the appropriate values from environment variables
When defining the DVC pipeline stage, I've intentionally left out an output dependency with the local dataset/ folder - i.e. the -o dataset part - as it was causing the azcopy command to fail. I think this is because DVC automatically clears the folders specified as output dependencies when you reproduce a stage.
When defining the azcopy command, I've included the --delete-destination="true" option. This allows synchronization of deleted files, i.e. files are deleted on your local dataset folder if deleted on the Azure container.
Please, bear with me, since you have a lot of questions. Answer needs a bit structure and background to be useful. Or skip to the very end to find some new ways of doing Is it possible to upload them in a human-readable format? :). Anyways, please let me know if that solves your problem, and in general would be great to have a better description of what you are trying to accomplish at the end (high level description).
You are right that by default DVC structures its remote in a content-addressable way (which makes it non human-readable). There are pros and cons to this. It's easy to deduplicate data, it's easy to enforce immutability and make sure that no one can touch it directly and remove something, directory names in projects make it connected to actual project and their meaning, etc.
Some materials on this: Versioning Data and Models, my answer of on how DVC structures its data, upcoming Data Management User Guide section (WIP still).
Saying that, it's clear there are downsides to this approach, especially when it comes to managing a lot of objects in the cloud (e.g. millions of images, etc). To name a few concerns that I see a lot as a pattern:
Data has been created (and being updated) by someone else. There is some ETL, third party tool, etc. We need to keep that format.
Third party tool expect to have data in "human" readable way. It doesn't integrate with DVC to being able to access it indirectly via Git. (one of the examples - Label Studio need direct links to S3).
It's not practical to move all of data into DVC, it doesn't make sense to instantiate all the files at once as one directory. Users need slices, usually based on some annotations (metadata), etc.
So, DVC has multiple features to deal with data in its own original layout:
dvc import-url - it'll download objects, it'll cache them, and will by default push (dvc push) to remote to again save them to guarantee reproducibility (this can be changed). This command creates a special file .dvc that is being used to detect changes in the cloud to see if DVC needs to download something again. It should cover the case for "to download data automatically from azure".
dvc get-url - this more or less wget or rclone or aws s3 cp, etc with multi cloud support. It just downloads objects.
A bit advanced thing (if you DVC pipelines):
Similar to import-url but for DVC pipelines - external dependencies
The the third (new) option. It's in beta phase, it's called "cloud versioning" and essentially it tries to keep the storage human readable while still benefit from using .dvc files in Git if you need them to reference an exact version of the data.
Cloud Versioning with DVC (it's WPI when I write this, if PR is merged it means you can find it in the docs
The document summarizes well the approach:
DVC supports the use of cloud object versioning for cases where users prefer to retain their original filenames and directory hierarchy in remote storage, in exchange for losing the de-duplication and performance benefits of content-addressable storage. When cloud versioning is enabled, DVC will store files in the remote according to their original directory location and
filenames. Different versions of a file will then be stored as separate versions of the corresponding object in cloud storage.
I have an application that uses about 20GB of raw data. The raw data consists of binaries.
The files rarely - if ever - change. Changes only happen if there are errors within the files that need to be resolved.
The most simple way to handle this would be to put the files in its own git repository and create a base image based on that. Then build the application on top of the raw data image.
Having a 20GB base image for a CI pipeline is not something I have tried and does not seem to be the optimal way to handle this situation.
The main reason for my approach here ist to prevent extra deployment complexity.
Is there a best practice, "correct" or more sensible way to do this?
Huge mostly-static data blocks like this are probably the one big exception to me to the “Docker images should be self-contained” rule. I’d suggest keeping this data somewhere else, and download it separately from the core docker run workflow.
I have had trouble in the past with multi-gigabyte images. Operations like docker push and docker pull in particular are prone to hanging up on the second gigabyte of individual layers. If, as you say, this static content changes rarely, there’s also a question of where to put it in the linear sequence of layers. It’s tempting to write something like
FROM ubuntu:18.04
ADD really-big-content.tar.gz /data
...
But even the ubuntu:18.04 image changes regularly (it gets security updates fairly frequently; your CI pipeline should explicitly docker pull it) and when it does a new build will have to transfer this entire unchanged 20 GB block again.
Instead I would put them somewhere like an AWS S3 bucket or similar object storage. (This is a poor match for source control systems, which (a) want to keep old content forever and (b) tend to be optimized for text rather than binary files.). Then I’d have a script that runs on the host that downloads that content, and then mount the corresponding host directory into the containers that need it.
curl -LO http://downloads.example.com/really-big-content.tar.gz
tar xzf really-big-content.tar.gz
docker run -v $PWD/really-big-content:/data ...
(In Kubernetes or another distributed world, I’d probably need to write a dedicated Job to download the content into a Persistent Volume and run that as part of my cluster bring-up. You could do the same thing in plain Docker to download the content into a named volume.)
I made a mistake my chaincode and installed them on the peers on my network. When I tried to instantiate the chaincode in the channels, I found the error.
Is there a way to debug chaincode before installing it on peers ? It seems to only get flagged when you instantiate it.
Is there a way to delete the chaincode from the peers without having to restart the network?
Depends on what you mean by mistake / debug. You should make sure it compiles first. That eliminates all typos, syntax, missing libraries, etc. But there is no way to debug functionality except to install and instantiate.
Technically, no. You can delete all the storage (/var/hyperledger/production/peer, /var/hyperledger/production/orderer, the kafka/zookeeper files, and CouchDB). Not a real big deal, but you do have to restart the network and recreate the channel, join it, install and instantiate the cc, etc. But you can install as a different name. Just change the name in your app connection definition to match. You can also upgrade by changing the version number but keeping the same name.
I just change the name until I get to a fairly settled spot and then do the deletes and restart all to clean up. A full cleanup (4 peers, 3 orderers, 4 kafka,3 zoopkeeper) takes me maybe 30 minutes. Normally, I keep a CLI open with install ccname1 and instantiate ccname1 in the buffer and can easily increment to ccname2,3,4,5. It only takes a few seconds that way.
If the error is (chaincode is already present in the peers)
You can try installing the chain code with different version number or different chain code name.
You can initiate chaincode in the channel only once. Next time you have to follow the procedure of upgrade chaincode steps.
Note : Before installing chain code you can check the syntax errors form the machine by installing go and compile the chain code.
I have an existing spark-job, the functionality of this spark-job is to connect kafka-server get the data and then storing the data into cassandra tables, now this spark-job is running on server inside spark-2.1.1-bin-hadoop2.7/bin but whenever I am trying to run this spark-job from other location, Its not running, this spark-job contains some JavaRDD related code.
Is there any chance, I can run this spark-job from outside also by adding any dependency in pom or something else?
whenever I am trying to run this spark-job from other location, Its not running
spark-job is a custom launcher script for a Spark application, perhaps with some additional command-line options and packages. Open it, review the content and fix the issue.
If it's too hard to figure out what spark-job does and there's no one nearby to help you out, it's likely time to throw it away and replace with the good ol' spark-submit.
Why don't you use it in the first place?!
Read up on spark-submit in Submitting Applications.
I built an Analysis that displayed Results, error free. All is well.
Then, I added some filters to existing criteria sets. I also copied an existing criteria set, pasted it, and modified it's filters. When I try to display results, I see a View Display Error.
I’d like to revert back to that earlier functional version of the analyses, hopefully without manually undoing the all of filter & criteria changes I made since then.
If you’ve seen a feature like this, I’d like to hear about it!
Micah-
Great question. There are many times in the past when we wished we had some simple SCM on the Oracle BI Web Catalog. There is currently no "out of the box" source control for the web catalog, but some simple work-arounds do exist.
If you have access server side where the web catalog lives you can start with the following approach.
Oracle BI Web Catalog Version Control Using GIT Server Side with CRON Job:
Make a backup of your web catalog!
Create a GIT Repository in the web cat
base directory where the root dir and root.atr file exist.
Initial commit eveything. ( git add -A; git commit -a -m
"initial commit"; git push )
Setup a CRON job to run a script Hourly,
Minutely, etc that will tell GIT to auto commit any
adds/deletes/modifications to your GIT repository. ( git add -A; git
commit -a -m "auto_commit_$(date +"%Y-%m-%d_%T")"; git push )
Here are the issues with this approach:
If the CRON runs hourly, and an Analysis changes 3 times in the hour
you'll be missing some versions in there.
No actual user submitted commit messages.
Object details such as the Objects pretty "Name" (caption), Description (user populated on Save Dialog), ACLs, and object custom properties are stored in a binary file format. These files have the .atr extension. The good news though is that the actual object definition is stored in a plain text file in XML (Without the .atr).
Take this as a baseline, and build upon it. Here is how you could step it up!
Use incron or other inotify based file monitoring such as ruby
based guard. Using this approach you could commit nearly
instantly anytime a user saves an object and the BI server updates
the file system.
Along with inotify, you could leverage the BI Soap API to retrieve the actual object details such as Description. This would allow you to create meaningfull commit messages. Or, parse the binary .atr file and pull the info out. Here are some good links to learn more about Web Cat ATR files: Link (Keep in mind this links are discussing OBI 10g. The binary format for 11G has changed slightly.)