Using Mind Maps

Mind maps is a visual way to structure information in a tree like structure. Such maps are convenient way for structuring knowledge, ideas, plans and tasks.

Usually, mind map starts with a root node, which has references to child nodes. References do not have names, but color can be use to code specific subjects. Each node, except root, has a parent node. Child represents some information specific to its parent node. This helps to build a tree of information, that can be easily consumed by human.

I am a big fan of visualization. Good visualization represents complicated things in a simple way that is easy to understand. Mind maps is one of the simplest but effective visualization techniques. It helps me in many different places: from making notes to managing projects.


I’ve spent a few days looking for mind map tool that I would love to use. At the end, I’ve picked MindNode, a beautiful tool available for macOS and iOS. It has a few very interesting features that I just love and won’t be able to live without anymore:
  1. There is an app for iOS, so I can work with mind maps using my iPhone and iPad. Experience is very similar to desktop application. Device app works smoothly; I never had issues with creating/updating maps from my phone.
  2. iCloud integration allows me to share maps between devices and computer.
  3. Multiple themes are available by default. Moreover, I can modify existing theme to create my own. I usually use different themes to differentiate different types of maps.
  4. Node can be marked as a task. For parent task, it will convert child nodes into tasks. Parent task can be used to track progress. Tasks mechanism is intuitive.
  5. MindNode allows to attach URL and description to node. It’s possible to create a documentation using mind map; MindNode can easily convert it to Markdown document.
  6. Additional relationship between nodes can be created using links. I do that quite often to connect two nodes that are in different parts of map.
  7. Awesome keyboard shortcuts support: can do most of work without touching touchpad.
MindNode has many more useful features, like: sharing mind maps with others using web interface, attaching media resources to node, exporting map into different formats etc.


As I’ve mentioned above, I use mind maps for different use cases. Most common are:
  1. building a knowledge
  2. generating ideas
  3. planning and managing projects
Next, I give more description on each of those.

Building knowledge

I found that mind maps work better for me than text notes. It is easier to read mind map: easy to detect most important parts, faster to read map in larger batches, references are an important mechanism to add extra dimensions to data. It is possible to collapse graph to see only the most important parts, and if need, dig deeper for more insights. Branches have different colors to separate them visually.

It is often hard to structure new mind map in the best way. I usually start with something that works right now, and do refactoring once gathered enough information, and there is a need in a new structure.

For me, map is a better way to collect and hold my notes. Before, I usually collected notes for each source of information (i.e. had different notes for different books). Therefore, it was hard to get through and find useful information. Things changed with mind maps - I collect knowledge per subject. For example, I have mind maps for such subjects as “Software Engineering”, “Writing”, and “Machine Learning”. I update those with facts, ideas, and best practices.

Here is example of my mind map for Software Engineering (all branches are collapsed for better visibility):

One of its sections focuses on making projects in a right way:

Such maps usually contain a branch for subjects that I want to research next. Those are represented as tasks, and help track my work on improving my knowledge base, follow up on missing parts etc.

Generating ideas

When working on new projects, it is important step to generate ideas for features, approaches and solutions etc.

I also found mind maps are quite helpful in this realm. When I need to brainstorm features, I start with a node “Features” and add child node for each high level feature. Afterwards, I extend feature node with more details and clarifications using its child nodes and so on and on.

Here is an example of features ideas for some service that I was scoping before. I start with some high level feature description, like “Integration with other tools”, and then move forward to clarify and add details.

And again, branch to track work on further research is a must:

Planning and managing projects

Most of the projects that I start nowadays begin with a new map in the MindNode. Map consists of the main parts: design, risks, scope of work, and plan of work.

I usually don’t go much into details in the map; I use another tools for documentation and sharing. Map stays private to me, as it has many details and thoughts that aren’t necessarily useful for others.

I leverage tasks support a lot in such mind maps. They help me to track work progress and also make sure there are no forgotten nuances and risks.

Because mind maps are so easy to read, many times I was able to find forgotten or missed work for various projects. This helped me a lot, as otherwise we would either get a bug during testing or wouldn’t be able to launch on time. I believe, only text notes couldn’t have helped me in such cases.

However, I do not use mind maps for anything that requires lots of text. Documentation, design notes aren’t part of my interaction with MindNode. I also do not use mind maps for actual tasks and tracking work. There are better tools for these use cases. However, where mind maps are great at defining ideas for design, adding pros and cons, risks and vision; maps are also great for defining list of milestones and high level stories.


If you’ve never tried mind maps before, and also looking for a way to manage through your life and on work, then give a shot to mind maps.

MindNode is a tool that I’d recommend the most, assuming you are using macOS/iOS and ok to pay around $30.

Message Locker


Extreme requirements require interesting solutions. Sometimes there is a need to come up with hybrid solution, that not always seems to be beautiful on first sight. One example is a Message Locker solution.

In service oriented architecture, application consists of many services that interact with each other.

Inevitably comes a need to fight high latencies caused by remote calls and necessary serialization/deserialization ( in a huge chain of dependent services calls, where each call results in a network hop with required fees like data marshaling and passing data through the network, adds at least a few milliseconds extra for each call.)

Service that requires to gather output from multiple dependencies to do its job is an aggregating service.

Such service needs to be smart at how it calls dependencies. If they are called one by one, then their latencies would be accumulated. The most obvious way to prevent this is to call each dependency in parallel. Now, service own latency would be defined mostly by its slowest dependency. In this case, we say that slowest dependency is in the critical path.

Aggregating service isn’t complex because it needs to call multiple services in parallel. And usually, there is simple way to avoid creating another service, if only business value it adds is aggregating output of multiple dependencies.

But, aggregating service becomes complex when:
  1. It adds complex logic on top of the data returned by dependencies
  2. It has to be sophisticated at orchestrating calls to many dependencies.
The need to orchestrate comes from the nature of SOA: sometimes Service need to call one or more dependencies first to gather the data necessary to call another dependency. Often it’s not possible to call all dependencies in parallel and just aggregate replies once all are available. In many cases, Service needs to call dependency A, to get the data necessary to call dependency B, results from which are required to decide if Service needs to call dependency C or D and so on.

Optimal call of dependencies is often the most important thing to do when fighting high latencies. And thus, eventually comes a need to have Aggregating service, that can call multiple dependencies in a savvy way.

But, even when there is an aggregating service in use already, inevitably comes a need to fight high latencies. And there are only so many ways this can be done:
  1. decrease latency for each dependency in the critical path (often by pulling dependencies of own dependency, and call them first)
  2. call dependencies in even smarter way.

This post stops on the 2nd way. If aggregating service already parallelizes calls to dependencies as much as possible and there is no way to make it even better, then, to be honest, not much can be done anymore.

Seriously, when service A needs to call dependency B so it can call dependency C later, what else can be done to save extra 10 ms you need that much?

That’s where Message Locker comes useful. It goes to a bit nasty territory to allow save additional milliseconds in aggregating service.

Message Locker

"Message Locker" means a Locker for a Message. Service allows to store a message in the some kind of locker, so only specific client can grab it. If message is not received during certain period, message becomes unavailable.

Message Locker is a distributed service that stores all the data in the memory. Client that sends a message into the locker is called sender. Client that receives message from locker is called receiver.

Each message is stored in the locker using a unique random key. When sender puts a message into the locker, it also provides additional attributes, like:
  1. TTL - time to store the message in the locker,
  2. Reads - number of times the message can be received.

Message would be removed from the locker whenever received for defined number of times or once its TTL expired. These rules prevent Message Locker to be bloated with obsolete messages.

Even after message was removed, Message Locker is still aware of it previous presence. Whenever receiver tries to get evicted message, it gets an error immediately.

In case receiver tries to get a message that is not evicted yet, it is returned to the receiver, and number of reads is increased. This approach doesn’t handles retries properly though.

In case receiver tries to get a message that is not sent yet, then Message Locker will hold the request until message becomes available or timeout happens.

How to use Message Locker?

Given 3 services A, B and C. Service A is an aggregator service, that calls multiple other services, among them services B and C. Service B has to be called before service C, as its output is part of input for service C. Service A also uses output of service B for own needs later as well.

Normally, service A would call service B, wait for reply and then call service C. During this workflow, service A needs to do following work before it can call C. This extra work becomes part of critical path:
  1. wait for reply from service B
  2. read reply from service B
  3. construct and call service C.
Network and extra serialization/deserialization are often expensive operations, and when one works with large amounts of data, could take 5-10ms. In this case, construction request and making remote call to service C also can add additional 5-10ms.

Without Message Locker

This is where Message Locker becomes helpful. Workflow is now changed: service A calls service B with key K, and in parallel calls service A with key K, B puts its reply into MessageLocker using key K, service C receive this reply using key K. Service A also receives service B’s reply from Locker using key K, and does this in parallel with service C call.

With Message Locker

In this case, there are following notable changes:
  1. time to construct and call service C happens in parallel with call to service B, and as such is removed from critical path
  2. time to deserialize request and do necessary initial work by service C is also execute in parallel with call to service B, and as such is removed from critical path
  3. time to deserialize reply from service B in service A also happens in parallel with call to service C, and as such is removed from critical path
  4. time to call to Message Locker, receive and deserialize received data by service C are added to critical path. This would eliminate savings added by #2.

Using Message Locker also adds complexities:
  1. Service A, B and C need to be integrated with Message Locker
  2. Service A or B needs to know how many times message would be received from locker or what timeout to use in order to not overload Message Locker with not need message and not cause issues with message being removed to fast.

Why not use existing solutions like...

Message Locker by itself is very similar to well known existing solutions: Message Broker and Distributed Cache. Although similarities are strong, there are a few differences, that make Message Locker to stand out for its own very specific use case.

Message Broker?

Message Broker would usually have a predefined list of queues. Producers would send messages to the queue and Consumers would consume. It is possible to create temporary queue, but it is usually expensive operation. Message Broker usually assumes processing latencies are less important than other traits, like persistence or durability or transactionality.

In this case Message Broker can’t be a good replacement for Message Locker.

Distributed Cache?

Message Locker is more like a distributed cache, with additional limitations. Message is stored only for 1 or few reads or very limited amount of time. Message is removed from locker as soon as it becomes “received”.

In ordinary caching, it is expected that content becomes available for much longer period than it is in Message Locker.


Message Locker is a way to decrease latencies in aggregation services by enabling additional parallelization. This is possible, as dependency between services are organized through a proxy - Message Locker. It holds the replies from dependencies and provides them to the receiver as soon as they are available. This allows to further hide expensive operations: network call and serialization/deserialization.

This comes with additional complexities:
  1. Right value, for timeout and number of reads to evict, can be error prone to define,
  2. Message Locker development and support can be cumbersome as well,
  3. Restructuring services to benefit from Message Locker.

But when there is no other choice and latencies had to be increased, Message Locker could be a solution.