So lets say I want to use other monitoring platforms such as ManageEngine and DataDog on Apache Airflow. So two ways we can maybe do this is one directly communicate it and also we could write a table that the other platforms can read. How would I do both these approaches so that that Airflow can tell these monitoring tools when the jobs start/stop.
Related
I have a questions regarding differences between Apache Airflow and Metaflow(https://docs.metaflow.org/). As far as I understood Apache airflow is just a job scheduler, that runs tasks. Metaflow from Netflix is as a dataflow library, which creates machine learning pipeline(dataflow is available) in forms of DAGs. Basically it means, that Metaflow can be executed on the Apache Airflow?
is my understanding correct?
If yes, is it possible to convert Metaflow DAG into Apache Airflow DAG?
Honestly, I haven't worked with Metaflow and thank you for introducing it to me! There is a nice introduction video you can find on Youtube.
Airflow is a framework for creating scheduled pipelines. A pipeline is a set of tasks, linked between each other that represent an Directed Acyclic Graph. Pipeline can be scheduled, you can tell how often or when it should run, you can tell when it should've ran in the past and what time period it should backfill. You can run the whole Airflow as one single docker container or you can have multi-node cluster, it has bunch of already existing operators to integrate with 3rd party services. I recommend to look into Airflow Architecture and concepts.
Metaflow looks like something similar, but created specifically for data-scientists. I can be wrong here, but looking at the Metaflow Basics it looks like I can the same way create a scheduled pipeline similar to Airflow.
I would look in specific tools you want to integrate with and which one of both integrates better. As mentioned, Airflow has lots of already made connectors and operators, as well as, powerful scheduler with backfill and Jinja template language to design your DB queries for enter link description here.
Hope that is somewhat helpful.
Here is also some nice article with feature comparison.
I am exploring the possible solutions for orchestrating my flows across multiple services via some infrastructure. Searching shows me a few options such as Conductor, Camunda, Airflow etc.
I am wondering what would fit my use case better
One of my service is in Java, the other is in Python
I need to pass info to the Java service, then take the output and pass it to the Python service
Final output is then published to another queue
It feels like Conductor is a good choice, but would love to hear your inputs!
All options can fulfill the requirement stated. Think about further / future requirements. Is it only a data pipe? Is it about orchestrating a larger end-to end business process? Do you need support for long-running processes? Is end-to-end transparency in a graphical form a benefit? Is graphical process modelling in BPMN2 standard going to be a benefit? Are there going to be audit or reporting requirements? Or is it going to be a simple, isolated, technical solution?
This article gives a great overview of tools in the market and what their primary use cases are: https://blog.bernd-ruecker.com/understanding-the-process-automation-landscape-9406fe019d93
All listed tools might technically be able to execute your workflow (I have no experience working with Conductor & Camunda). A few characteristics on which a decision is usually made are:
open vs closed source
how do you define workflows? (e.g. Python code in Airflow. Others use e.g. JSON/XML/something custom)
does it come with a UI?
can it scale out in case my workloads start growing?
is it agnostic to any technology or limited to running certain technologies? (e.g. Oozie is built for scheduling jobs on Hadoop)
other requirements could be e.g. security, logging, monitoring, etc.
There are many orchestration-tool-comparisons on the internet, e.g. 1 or 2.
Introduction to Container Orchestration
The practice of automating the administration of container-based microservice applications across different clusters is known as container orchestration. Within corporations, this notion is gaining popularity. In addition, a variety of Container Orchestration technologies have become indispensable in the deployment of microservice-based applications.
Software development in the modern era is no longer monolithic. Instead, it generates component-based apps that run across many containers. These adaptable and scalable containers work together to accomplish a specified purpose or microservice.
Depending on the complexity of the application and other requirements like load balancing, they may span many clusters.
Containers encapsulate application code as well as its dependencies. To function efficiently, they receive the resources they require from physical or virtual hosts. When complicated systems are built as containers, clustering them for deployment requires adequate management and priority.
How to Choose a Container Orchestration Tool?
We've looked at a number of Orchestration Tools that you may examine when selecting which is ideal for your business. To do so, make sure to understand your company's requirements and operations. Then you'll be able to more readily weigh the benefits and drawbacks of each option.
Kubernetes
Kubernetes has a lot of features and is ideally suited for container and cluster management at the corporate level. Kubernetes is managed by a number of platforms, including Google, AWS, Azure, Pivotal, and Docker. As the containerized workload grows, you have a lot of options.
The biggest disadvantage is that it does not work with Docker Swarm and Compose CLI manifests. It might also be difficult to understand and set up. Despite these flaws, it is one of the most used systems for cluster deployment and management.
Docker Swarm
For individuals who are already familiar with Docker Compose, Docker Swarm is a better option. It's easy to use and doesn't require any additional software. Unlike Kubernetes and Amazon ECS, however, Docker Swarm lacks sophisticated features such as built-in logging and monitoring. As a result, it is better suited to small-scale businesses that are just starting started with containers.
Amazon ECS
If you're already familiar with Amazon Web Services, Amazon ECS is a great way to install and configure clusters. It's a quick and easy method to get started, and it scales to match demand. It also connects with a number of other AWS services. It's also excellent for small teams with limited resources for container maintenance.
One of its disadvantages is that it is incompatible with nonstandard deployments. It also contains ECS-specific configuration files, which complicates debugging.
After some research and testing, we have decided to start using Google Cloud Composer. Since our current DAGs and tasks are relatively small, and don't require the server to run continuously, I am looking how to manage costs.
Two questions:
The option to use preemptible VMs seems logical. This saves costs considerably, and I'm thinking to go for 3x n1-standard-4. I expect each task to be quite short, so don't think this will have significant impact for our workloads. Is it possible to use preemptible VMs with Composer?
Schedule to turn the Composer environment on/off, as asked in this post. I can't find how to do this in the documentation, either by turning the whole enviroment down, or to shutdown the workers as proposed in the answer.
Help, anyone?
This is an interesting question.
One roadblock you may encounter is the nature of Airflow itself. Generally, Airflow is not intended for use ephemerally. Instead, I'd suspect that the vast majority of Airflow use, Cloud Composer or otherwise, is persistent. Ephemerality brings cost benefits but also risks with Airflow architecture. For example, what happens if the scheduler to restart your Airflow resources fails?
To answer your questions:
Preemptibles are not supported in Composer. While PVMs have a ton of awesome benefits, they could leave tasks in a very weird state, especially if you got preempted several times.
There is not formal documentation for this process because it's generally informal and not recommended if you must depend on your environment. The basic approach, though, would be to:
Create a very small GCE VM
Setup the Cloud SDK (gcloud) to connect to your project
Create a crontab that either does a fresh create/delete of an environment when you need it /or/ pauses the VMs in the Composer worker pool
In the long-term, I think Composer will better support ephemeral use of worker resources. In the short term, another option is to run a lightweight Airflow environment on a small(ish) GCE VM and then suspend/resume that VM when you need to use Airflow. You don't get Composer that way, but you do benefit from the team's work improving and expanding GCP support in core Airflow.
I have the IBM Business Process Manager Advanced 7.5 installed.
Question:
Is it possible to install and run newer version - IBM BPM 8.5 on the same machine?
I worry about ports conflict (for example port 9043 to IBM Console).
Maybe I should ask how to change default port configuration?
Please help.
Technically it can be possible, however I suggest you do not do this as ibm bpm requires a lot of system resources to run and installing two versions of ibm bpm can make the system slower than ever before.
However I have seen multiple instances of same ibm bpm version running on a single cluster on server VM. This is practically stable and in use from considerable tenure.
PS. - I had administered a huge ibm bpm infra containing 80+ ibm bpm servers.
As Gas already commented, in theory this is possible. But you have to be aware, that IBM BPM is not only using the specified ports for web access, it also uses ports for internal communication. In my opinion, this is not an easy task to get right.
On the other hand, the system requirements for IBM BPM are quite challenging for the server, if You want to run both instances in parallel, you should consider that your server will need to be capable. WebSphere is kind of greedy and not really designed to share its resources ;)
Yes, you can run multiple versions of BPM on the same system. The primary concerns are going to be port conflict and OS system resources. Use the BPMConfig to create a new profile and installation that is on different ports. On my lab machines with VMs, I install all the BPM installs with the default ports and only have one (1) running at a time. If I need 2, I just spin up a new VM from the base template and go from there.
By Default, the port conflicts are addressed by the WebSphere Application server code. If needed you can specify "initialPortAssignment" for Dmgr, node and cluster members while creating the environment using BPMConfig command. You can even specify specific port numbers using the
https://www.ibm.com/support/knowledgecenter/en/SSFPJS_8.6.0/com.ibm.wbpm.ref.doc/topics/samplecfgprops.html
You can also provide Websphere options like "-startingPort starting_port | -portsFile ports_file_path | -defaultPorts" for Dmgr bpm.dmgr.profileOptions= and nodes bpm.de.node.#.profileOptions in the BPMConfig properties file. For cluster members just have option to indicate the starting port.
REf: https://www.ibm.com/support/knowledgecenter/cs/SSAW57_8.5.5/com.ibm.websphere.nd.multiplatform.doc/ae/rxml_manageprofiles.html
I would not advise on changing the port numbers once you start using the BPM environment.
As indicated by others make sure you have enough resources if you are planning to run both environments at the same time.
Yes, I am using two versions for evaluation. Port conflicts can be handled using server (WebSphere Integrated Solutions Console) console or BPMConfig utils.
I want to create a modestly scalable development environment for an in-development web service.
Ideally, there would be an nginx web server with haproxy and a few database servers, websockets, the works.
I'd be going with Amazon cloud services for all of this hosting... but I'd rather not pay for CPU cycles when I'm just developing... much less develop on a remote, cloud environment.
What's the best way to go about modeling a somewhat complex development environment locally that could - hopefully, at the press of a button - sync with a similarly architected Amazon cloud environment?
All I have is my Macbook Pro. I also have a fully built 1Ghz tower computer in the closet I could leverage, if needed, and wouldn't be opposed to buying more. But, ultimately, I'd like to have the ability to sync to production with minimal steps and reconfiguration.
Thanks!
Check out vagrant and virtualbox. That will get you local environments running nicely on your macbook. Syncing to EC2 is going to be tougher. At the system level you'll want to use something like puppet or chef (which are both nicely supported by vagrant). Add to that a solid automated application deployment mechanism and you should be close. Be prepared to put some time into this, it's not likely to be a trivial undertaking.