Last week I tried out diagrams to knock up some cloud infrastructure diagrams. There are several things I really like about this tool:

• The learning curve is very easy. I was able to absorb the key concepts and produce a useful diagram showing the AWS setup for an application I am working on within about 30 mins of installing it for the first time.
• The [effort in]:[pretty pictures out] ratio is very satisfying.
• Because the diagram is generated from code, it can live in your repo. The diff changing the diagram could be in the same commit as the updates to your CDK definitions or ansible playbooks or whatever it is that actually makes the infrastructure changes.

For example, the following diagram

is generated from this short python snippet:

from diagrams import Cluster, Diagram
from diagrams.aws.compute import Fargate
from diagrams.aws.database import RDS, ElastiCache
from diagrams.aws.engagement import SES
from diagrams.aws.network import ELB, Route53
from diagrams.aws.storage import S3

with Diagram("", show=False):
    ses = SES("Mail Transport (SES)")
    dns = Route53("Route 53 (DNS)")
    s3 = S3("S3")

    with Cluster("VPC"):
        lb = ELB("Load Balancer (ALB)")
        elasticache = ElastiCache("Redis (ElastiCache)")

        with Cluster("ECS"):
            web = Fargate("web")

        with Cluster("DB Cluster (RDS)"):
            db_primary = RDS("primary")
            db_primary - RDS("read replica")

    dns >> lb
    lb >> web

    web >> elasticache
    web >> db_primary
    web >> s3
    web >> ses

These are some of my favourite programming bloggers. All 3 of these are consistently knocking out quality posts and well worth a follow:

1. Martin Heinz
2. Adam Johnson
3. Roman Imankulov

I've mentioned Will McGugan's excellent library Rich on this blog before. It is a great tool for building nice terminal interfaces, but it is also an important local development tool. Here's three top tips:

1. Rich can be registered as a handler to render stacktraces. As well as the aesthetics, using Rich to handle stacktraces like this provides additional context which improves the usefulness of error messages in comparison to python's default handler.
2. Rich.inspect can be used to examine a python object at runtime. I used to use dir() or vars() for this, but rich.inspect() is a big step up.
3. Rich can be used as a log handler. The docs cover how to use it with python's logging module, but Will has also published this blog post showing how to configure Django to use Rich as the default log handler.