Friday, December 30, 2016

Making Technology Choices for Personal Growth.

When I started doing IT as a profession, the number of available, commercially used technologies was considerably less. Back then, most of IT ran on IBM mainframes. The number of tools you needed to learn to be marketable could be counted on one or two hands.  Today, there are far too many technologies/products/frameworks and not nearly enough time to learn them all.  Should I invest in AngularJS or React? Java8 or Scala? Amazon or Azure? Python or Node.js?  The list goes on and on.

If you're like me, your time to learn new products or frameworks is spare time. It's limited. There's just not enough spare time to learn everything, or even close. I often get asked whether they should learn this product or that framework. Very few ask about how they should be making their time investment choices. Let's face it, with so much educational material available on the internet for free or very low cash outlay, it's really a time investment we're talking about.

I look at learning technologies the same way I look at investments. Most software vendors, be they open source or commercial, publish documentation our even the ability to download and install most products you might consider for free. Thanks to Udemy and Amazon, who have pushed prices for online courses and eBooks to unbelievably low levels, cheap resources are available for the more popular choices. There's very little cash outlay for materials. However, it's an investment all the same. The investment is more time than cash.

I believe time is money. Time invested is time I can't spend consulting, writing, or anything else I do to make money. Consequently, I try to make good decisions as to which technologies I invest time in.  That begs the important question: how do you choose?  Furthermore, there's another dimension to this choice: how much time do you invest in one of those choices?

The amount of time you spend learning a new product, technology, or framework has diminishing returns. That is, the more time you invest, the less incremental payback you'll get for the investment. Like stocks, you don't necessarily buy a large portion to start out. Often, you invest a little and as the market develops, sometimes you buy more. Time investments in new technologies are the same way. 

Like financial investments, investing your time has risk. Any time spent learning a new technology, product, or framework that never takes off is time you'll never get back. Your objective is to consciously manage that risk. 

Research Tactics

There are several tactics I use to help me decide which technologies to invest in. Here are some of mine.

Ask a mentor or person whose opinion you value about technology choices you're considering. It's quick and easy. That person may have thought of issues or concerns that you haven't thought of so far. Often verbalizing your thoughts to another person helps you crystallize and more thoroughly think through your line of thought. 

Look at what the market values. 
The best way to do this is to use the Job Trends site from Indeed is a job posting site where hunters post resumes and firms or recruiters post jobs. Indeed keeps history and lets you graph postings and hunter skills listed on their resume over time. As an example, I'm using this comparison that compares top cloud vendors, perhaps for people looking at learning cloud technologies.

You want technologies with an upward job posting trend. You want technologies that firms value and recruit for. Given the time you'll need to invest, you want a realistic chance of a payback for your investment. The market can change at any time and the landscape might look different in six months, but hard numbers are more attractive than gut feelings. I've posted the job posting comparison of cloud vendors (taken on Dec. 30, 2016). I'm using Amazon Web Services, Azure, and Google Cloud in my comparison.

You can see that Amazon AWS job postings are on a sharp uptrend followed by a slightly less uptrend for Azure postings. It would be safe to infer that AWS skills have more of a market right now than either Azure or Google Cloud. If you believe I've left off vendors that you care about, you should surf to the site and change the vendors to your liking.

You want technologies with a postings vs.  seeker interest ratio of at least 1.00. Anything less means that there might be oversupply in the market and that can drive salaries/consulting rates down. I say "might" as not all job seekers are honest. Some look for jobs with skills they don't have. With the demand I hear about for AWS skills from recruiters, I believe the 1.02 seekers per posting ratio to be overstated and there are more postings per seeker. Note: Pay attention to the description: "seekers per posting" vs. "Posting per seeker".

Test your search criteria. Toward the bottom of the comparison page, you're offered links that will list job postings for that particular search criteria. You should look at a sample to make sure your search term isn't picking something you're not interested in. For example, if one were to enter a term too general, you could be including irrelevant postings and seekers in your comparison.

A few words of warning: Indeed data has limits. Here are a few:
  • This data doesn't account for current labor salaries/rates. For example, "HTML" looks hot until you figure out that market rates for that skill are really low.
  • The data presented is three months old and the current graph might be slightly different.
  • This data works best when you enter competing products or frameworks. Comparing AWS to Java isn't useful; market drivers for those two are completely different and you'll get more double counting of postings (posts might have both terms).

Look at what people are interested in

Before technologies are listed in job postings or on resumes, they are searched for on the web. Google maintains search term history that it graphs or allows you to download. Furthermore, as with the Indeed Job Trend site, you can compare search data for multiple searches. As an example, I've graphed the same search criteria we used above on the Indeed site.

Internet search results appear to be congruent with what we see on the Indeed Job Trends site. The difference between AWS and Azure does appear to be less stark than what we see on the Indeed site.  Here are some things to keep in mind:
  • It's important to test your search results just like with indeed.
  • Trend data for technologies with common names will be meaningless.
  • Poor paying technologies might have rising interest too just like on the Indeed site.
Deciding How Much Time to Invest

Deciding how much time to invest is more difficult. Everyone has different experience levels, talents, and abilities. Some technologies might take more investment than others. What amount I might need might be different than the amount you need. Here are some tactics I use.

You must invest in something all the time. Pick a commitment level; two hours a week, four hours a week, or even more. This should become a habit you don't even think about. If you don't you'll become stagnant. Your skills will get dated over time and your marketability will gradually decrease. Furthermore, you won't develop tactics for learning new things quickly. When you wake up and realize that you're very out of date, it'll take a long time to catch up. Unlike financial investments where you can adopt a cash position, it's too dangerous in a technology world to sit on the sidelines.

Time-box your investment. That is, set a rough amount of time you'll spend on a technology choice up front and then re-assess what you're willing to spend after that learning period. I typically use two hours or four hours as an initial time frame limit. That said, over the past couple of decades, I've honed tactics that allow me to spend less time than many others. Two hours might not be enough for you or it might be too much. You'll need to decide the amount. It's important that you roughly track the time you spend. Also, feel free to quit early if you learn enough to make the decision that you're not going to invest additional time.

Time-boxing mitigates your investment risk. What you want to avoid is going down a rabbit hole and spending boat loads of time unproductively. It also keeps the time you're spending at the forefront of your mind. You'll never get this time back.

Distinguish between "exploratory" learning and "objective" learning. Objective learning has a defined purpose such as doing upcoming work at your current employer or gearing up for a new job search or interview. Exploratory learning is for general knowledge you can use with colleagues. Exploratory learning doesn't require as much depth. 

There's value in learning the basics and general advantages and disadvantages of a product. With general knowledge, you know whether or not you want to pursue work using this technology. You will know enough about a technology to participate intelligently in conversations with other developers or recruiters. You know enough to come back later and dig more deeply if the need arises (you decide to submit for a job that needs it). Some developers will resist this idea, but you don't need an advanced, in-depth knowledge of every product you become acquainted with.

Don't learn technologies in depth until there's a high probability you'll will use them at work or need them to support a job search. Technology moves too quickly for that. Your newly learned in-depth knowledge becomes dated quickly. Looking at this in investment terms, don't invest until there's a reasonable probability of a payback. Another way to think about this is that classic YAGNI applies. 

Thanks for taking time to read this post. I would like to hear your thoughts on this topic.

Sunday, December 11, 2016

Automated Integration Testing in a Microservices World.

Everyone's dabbling with microservices these days. It turns out that writing distributed applications are difficult. They offer advantages to be sure. However, there is no free lunch. One of the difficulties is automated integration testing. That is, testing a microservice with all the external resources it needs including any other services it calls, the databases it uses, any queues it uses. Just setting up and maintaining these resources can be a daunting task that often takes specialized labor.  All too often, integration testing is difficult enough that the task is sloughed off. Fortunately, Docker and it's companion product Docker Compose can make integration testing much easier.

Standardizing on Docker images for deployment artifacts makes integration testing easier. Most organizations writing microservices seem to adopt Docker as a deployment artifact anyway as it greatly speeds up interaction between application developers and operations. It facilitates integration testing as well as you can deploy services you consume (or mocks for the services you consume) temporarily and run integration tests against them. Additionally, consumers for your service can temporarily deploy your docker image to perform their own integration tests. However, as most services also need databases, message queues, and possibly other resources to function properly, that isn't the end of the story. I've previously written about how to dockerize (is that a word?) your own services and applications here.

Docker images already exist for most database software and message software. It's possible to leverage these docker deployments for your own integration testing. In other words, the community has done part of your setup work for you. For example, if my service needs a PostgreSQL database to function, I leverage the official Docker deployment for my integration tests. As it turns out, the Postgres docker deployment makes their image very easy to consumer for integration testing. All I need to do is mount the directory '/docker-entrypoint-initdb.d' and make sure that directory has any SQL files and/or shell scripts I need run to set the database up for use by my application.  The MySQL docker deployment does something similar. For messaging, similar docker distributions exist for RabbitMQ, Active MQ, and Kafka. Note that ActiveMQ and Kafka aren't yet "official" docker deployments.

Docker Compose makes it very easy to assemble multiple images into a consistent and easily deployable environment. Docker-compose configurations are YAML files. Detailed documentation can be found here. It is out of scope for this blog entry to do a complete overview of Docker Compose, but I'll point you to an open source example and discuss a couple of snippets from the example as an illustration.

The screen shot on the left contains a snippet of a docker-compose configuration. The full source is here. Note that each section under services describes a docker image that's to be deployed and possibly built. In this snippet, images vote, redis, worker, and db are to be deployed. Note that vote and worker will be built (e.g. turned into a Docker image) before they are deployed. For images already built, it's only necessary to list the image name.

Other common compose directives are as follows:
  • volumes-- links a directory in the real world to a directory inside the container
  • ports-- links a port in the real world to a port on the inside of the container. For example, vote links port 5000 on the outside to port 80 on the inside.
  • command-- specifies the command within the Docker container that will be run at startup.
  • environment-- (not illustrated here) allows you to set environment variables within the Docker container

Assemble and maintain a Docker compose configuration for your services. This is for your own use in integration tests and so that your consumers can easily know what resources you require in case they want to run integration tests of their own. It's also possible for them to use that compose configuration directly and include it when they set up for their own integration tests.

The Docker environment for your integration tests should be started and shut down as part of the execution of the test. This has many advantages over maintaining the environment separately in an "always on" state. When integration tests aren't needed, they son't consume resources regardless. Those integration tests, along with their environment, can be easily run by developers locally if they need to debug issues; debugging separate environments is always more problematic. Furthermore, integration tests can be easily and painlessly be hosted anywhere (e.g. on-premise, in the cloud) and are host agnostic.

An Integration Test Example

I would be remiss if I didn't pull these concepts together for an integration test example for you.  For my example, I'm leveraging an integration test generic health check written to make sure that a RabbitMQ environment is up and functioning. The source for the check is here, but we're more interested in its integration test today. 

This test utilizes the DockerProcessAPI toolset as I don't currently work in environments that require a docker-machine and the Docker Remote API (Linux or Windows 10 Pro/Enterprise). If your environment requires a docker-machine (e.g. it is a Mac or an earlier version of Windows), then I recommend the Spotify docker-client instead.

The integration test for the health check uses Docker to establish a RabbitMQ environment before the test and shut it down after the test. This part is written as a JUnit test using the @BeforeClass and @AfterClass annotations to bring the environment up once for the entire test and not for each test individually.
In this example, I first pull the latest RabbitMQ image (official distribution). I then map a port for RabbitMQ to use and start the container. I wait five seconds for the environment to initialize, then cause a logging for the current docker environment running.

My log of what Docker containers are running isn't technically required. It does help sometimes if there are port conflicts where the test is running or other problems with a failed test that need to be investigated. As this test runs in a scheduled manner, I don't always know execution context.

After the test completes, the @AfterClass method will shut down the RabbitMQ instance I started and once again cause a container listing just in case something needs to be investigated.

That's a very short example. Had the integration test environment been more complicated and I needed Docker Compose, that would have been relatively simple with the DockerProcessAPI as well. Here's an example of bringing up a Docker Compose environment given a compose configuration YAML:

Here's an example after the test of bringing that same environment back down:

In addition, there are additional convenience methods on the DockerProcessAPI that can log compose environments that are running for investigative purposes later.

Thanks for taking time to read this entry. Feel free to comment or contact me if you have questions.  


Monday, November 21, 2016

Book Review: Reactive Services Architecture - Design Principles for Distributed Applications

A friend of mine gave me a copy of the new book "Reactive Services Architecture - Design Principles for Distributed Applications" by Jonas BonĂ©r.  My friend was quite taken by the content and asked me what I thought of it. Having been working with microservices architecture and writing about them for a couple of years, I was intrigued and took up the gauntlet.

I was immediately struck by the word "Reactive" and wondered what was the difference between a "reactive" microservice and a non-reactive microservice. I started reading the book with this question nagging me along the way.

Book Summary

The book starts out by a traditional defining of the problem with monolithic applications; These are applications that have grown too large and complex to maintain and enhance with any reasonable ease and speed. It has a very well written introduction to the concept of microservices architecture and how it solves the problems presented by monolithic applications. The book describes basic principles of microservices architecture and also effectively compares microservices to traditional SOA in a manner that's very easy to read and understand. Principles described by this sections are:
  • Microservices do one thing and do it well; they have a single functional (e.g. business) purpose.
  • Microservices most often evolve from monoliths; not often used for completely new applications
The second chapter refines the single responsibility trait that microservices have and introduces us to several additional microservice principles. They are principles that are present in previous discussions of microservice architectures and aren't really new, but are explained very clearly and concisely.  These principles are:
  1. Microservices act autonomously; they are context independent.
  2. Microservices own their own state / data store.
  3. Microservices should have location transparency; they use a service discovery mechanism so they can scale effectively and have clustering / resilience.
  4. In a microservice world, what can be made asynchronous or non-blocking, should be made asynchronous (some term this "eventually consistent").
  5. In a microservice world, planning for failure is a necessity.
I did find the discussion on isolation at the beginning chapter two extremely interesting. Isolation is presented as a coupling of a service to time (when execution occurs) and space (where the service is hosted). Removing this coupling (i.e. isolating services) is a prerequisite to adhere to principles 1, 3, 4, and 5 above. I had never explicitly identified this type of coupling previously in my writing and consider it insightful. 

The author does discuss reliance on messaging technologies as a part of the definition of a "reactive" microservice. I look at messaging as one of many techniques for making service calls asynchronous. I see messaging as a technique for supporting principle 4 and mitigating 5 and not really fundamental to the architecture. Usually, architecture definitions are principle based and not reliant on a specific implementation tactic. I agree with the author in that designers usually "assume" real-time needs instead of challenging them and making more portions of a system asynchronous. The author seems to assume the opposite. To be fair on pp 36-8 (chapter 3), the author does acknowledge that there are portions of a system that must be synchronous.

There are reasons for using ("persistent") messaging to make service calls asynchronous rather than spawning the work in a separate thread. Work in a spawned thread will be lost if a service instance dies where it won't be if persistent messaging is used. The author really doesn't discuss this point or address why "messaging" (persistent or not) is the only option he presents for making work asynchronous.

Chapter three discusses several cross-cutting concerns that frequently come up with people newly introduced to microservice architectures. They are:
  • Service discovery as a means to support location transparency.
  • API Gateways as a way to manage evolving contracts over time.
  • Messaging as a way to support several of the principles outlined above.
  • Security management 
  • Minimizing data coupling and coordination costs
The author does point to event driven architecture and conflict-free replicated data types (CRDTs) as being natural complements to microservice architectures. Essentially, the event-log becomes the source of truth for a microservice and the underlying database is simply a convenient "cache" of that truth. These concepts are touched on, but not really explored in depth. To be fair, these are weighty topics and likely deserve books of their own; declaring in-depth discussion of them "out of scope" for this book is reasonable.

Reviewer Summary -- Rates 4 Stars out of 5

This book is a great summary for those looking for an overview of microservices architecture. All concepts are explained concisely, clearly, and in an easy to understand writing style. As Lightbend is handing out free copies (at the time of this writing), it's certainly cost-effective.

This book respects the readers time. Time is a scarce resource for me as I'm sure it is for many. At 47 pages, it can be easily be read in one or two sittings.

This book doesn't try to sell products. When I saw the company name "Lightbend" on the cover, I was a little nervous that there would be plugs for Lightbend products. There isn't. Akka is mentioned as an implementation option as well as Apache Camel and other products. Nothing, however, that's overt marketing.

I'm not convinced that a "reactive" microservice is any different than a normal microservice. While I agree that all service calls that can be non-blocking/asynchronous should be, that's not really new or different. It is a microservice design best practice to be sure, but not really a different architecture style. The book does contain mentions of reactive programming and the Reactive Manifesto. But not really enough to link the books content specifically to those constructs for me. In the end, it's this point that kept my rating out of the five-star category.

You might wonder why I published this review on my blog instead of on Amazon. As a book author myself in this genre, Amazon will not publish my reviews on other computer-related books. Thanks for reading this article.

Friday, September 23, 2016

Using Java Thread Dumps to Diagnose Application Performance

On a holiday weekend last year, I got an emergency call from a client. One of their Java EE applications would freeze and stop servicing users within an hour after container start-up. I was called in to help investigate. I started off by requesting a thread dump and memory dump of the container once it had stopped accepting requests. It's the thread dump that I'm focusing on today.  That Java thread-dump is here (package scrubbed to protect the client).

During that exercise, I noticed that when I analyze thread dumps, I look for the same things. Whether it's a performance issue or some sort of freezing issue, I manually scanned the thread dump for the same types of conditions. This year, working for another client, I'm faced with a performance tuning exercise that will likely require analysis of numerous thread dumps and wasn't looking forward to the busy work. That prospect got me to do some introspection and figure out exactly what I look for and find or build a product that does this.

Threads that block other threads

Most developers know the syntax behind using Java's synchronized keyword and that it's used to ensure that one and only one thread executes a section of code or uses a given Java resource at one time. I'm not going to digress into a discussion of lock monitors and coding issues; if you need a refresher, please see this article. Most experienced developers use synchronization with extreme care as bugs with synchronization are intermittent and extremely hard to diagnose and fix.

Frequent symptoms of synchronization issues are performance problems or cases where applications freeze and no longer accept client requests. Essentially, synchronization causes other threads servicing other client requests to wait until the needed Java resource is available for use. Those waiting client threads are typically in a BLOCKED state. I immediately suspected this type of issue in diagnosing the issue I was investigating over the holiday last weekend.  Here's a sample of a BLOCKED thread entry in a thread dump:

"http-bio-" daemon prio=6 tid=0x000000001cf24000 nid=0x2054 waiting for monitor entry [0x0000000022f9c000]
   java.lang.Thread.State: BLOCKED (on object monitor)
 at java.beans.Introspector.getBeanInfo(
 - waiting to lock <0x0000000680440048> (a com.sun.beans.WeakCache)
 at org.apache.axis.utils.BeanUtils$
 at Method)

Note that the dump explicitly lists that it's waiting on resource 0x0000000680440048 which is owned by this thread:

"http-bio-" daemon prio=6 tid=0x000000001bf06000 nid=0x21b0 runnable [0x000000002e0dd000]
   java.lang.Thread.State: RUNNABLE
 at java.lang.Class.getDeclaredMethods0(Native Method)
 at java.lang.Class.privateGetDeclaredMethods(
 at java.lang.Class.privateGetPublicMethods(
 at java.lang.Class.getMethods(
 at java.beans.Introspector.getPublicDeclaredMethods(
 - locked <0x0000000680440048> (a com.sun.beans.WeakCache)
 at java.beans.Introspector.internalFindMethod(

It turns out that more than one thread was waiting on this resource.

IO bound threads

One frequent source of application performance issues are threads that are waiting on Input/Output to occur. This often takes the form of a database read or write or a service call of some type.  Many developers assume that most performance issues are caused by slow database access and start looking at SQL queries. I do not make this assumption. Furthermore, if it is a database tuning issue, you need to identify the specific SQL that needs to be tuned. At any rate, if the source of your performance issue is IO, thread dumps can help you identify where in your code the issue is taking place. 

Here is an example thread that's IO-bound:

"QuartzScheduler_Worker-2" prio=6 tid=0x000000001abc7000 nid=0x2208 runnable [0x000000001df3e000]
   java.lang.Thread.State: RUNNABLE
 at Method)
 at net.sourceforge.jtds.jdbc.SharedSocket.readPacket(
 at net.sourceforge.jtds.jdbc.SharedSocket.getNetPacket(
... (many thread stack entries omitted for brevity)
        at com.jmu.scholar.dao.FormRuleDAO.findByFormId(

Note that within the thread stack, there's an explicit reference to application code that's initiating the IO. In this case, IO is being initiate by a database query in a specific application method. 

Note that just because the dump caught this one occurrence of this query, doesn't mean it's a performance issue. If, however, a large percentage of running threads are IO-bound in the same method, then this database access would become a tuning target. To tune this specific database access, a developer can focus on this one query instead of looking at all queries within the application. How to tun the database access is out of scope for this blog entry.

Performance Hot Spots

Most developers upon being asked how to tune an application in an interview will tell you to use a Java profiler. That answer misses the point. A profiler helps you tune a specific section of code after you've identified the section of code that needs to be tuned. Often performance issues show up in production and it's not possible to run a profiler on your container in production. 

A thread dump taken in production on an active application can help you identify which section of code needs to be tuned, perhaps with a profiler. Furthermore, thread dumps are unintrusive enough that you can take them in production without material impact to users.  

To see how dumps help, let's review how a profiler works. A profiler works by taking a thread dump periodically, perhaps every 5 milliseconds. That thread dump specifies where in your code you're spending time. For example, if your test causes the profiler to take 100 samples and method appears in 33 of them, then your spending 33% of your time in that method. If that's the method with the highest percentage, that is where you'll often start tuning.

In fact, thread dump data is better than profiler data in several ways:
  • It measures what's actually happening in production vs. a profile of a specific business process or unit test case.
  • It includes any synchronization issues between threads that won't show up in a profile of one thread in a unit test case (there are no other threads to contend with).

The problem is collecting the data. Yes, thread dumps are easy to collect. Counting occurrences of method references in running threads is laborious, tedious, and annoyingly time consuming.

Introducing StackWise

The first thing I did was scan for products that analyze thread dumps for these types of issues. There are many products that analyze thread dumps, but they tend to be interactive tools that summarize threads and allow you to selectively expand and contract them. A couple categorized threads by state (e.g. RUNNING, BLOCKED, etc.). However, none of these really looked for the three items I look for. Hence, I created the product StackWise, which is open source and freely available for you to use.

StackWise will analyze a thread dump and report useful information on all three of these conditions.  A sample of StackWise output can be found here.  Note that you get the following items of information:
  • The percentage of threads that are IO bound and a summary of those IO-bound threads.
  • Threads that are locking resources needed by other threads.
  • A list of application method reference counts listed in descending order. 
In interpreting performance hot spots, StackWise will report application methods in which you're spending the most time. Methods belonging to ServletFilter classes can be ignored as they are often listed in all running threads.  Other method mentions, however, are possible tuning targets.

If you analyze thread dumps in ways other than what StackWise already covers, I'd like to hear your ideas.  Thanks for reading this entry.

Sunday, September 4, 2016

All Monoliths Are Not Created Equal

All monoliths are not created equal. That is, not all monoliths became monoliths the same way nor do they have the same root causes. With this post, I'll identify different types of monoliths I've seen. This is useful as the tactics I use to break the monolith into smaller, more manageable, pieces is different depending on which category of monolith I'm dealing with. It's worth noting that some times elements of these different categories of monoliths are used in combination.

A monolith is an application that has grown too large to effectively manage. That is, it's expensive to enhance and fix. Change to monoliths often come with unintended consequences; you fix something and other things break. Monoliths effectively marry you to a technical stack, making it difficult to grow with new technologies as they evolve and mature. Due to the size and complexity of underlying monolithic code, it's often expensive and time-consuming to bring on new developers (there's so much to learn). The same labor characteristic limits the business in that outsourcing change often isn't an option.  

Feature-Bloated Web Application
This type of monolith is specific to web applications. It becomes larger and more complex as new features are added. Moreover, some of those new features weren't considered when the application was originally designed. Consequently, those new features are often inelegantly "tacked on" and don't really conform to the initial design of the application. Often, there's no time and budget to revamp the application design to add new features in ways that are easy to maintain and support. The consequence for this is often technical debt in the form of unwanted coupling.

New features add complexity to all parts of the application. New features impact not only the user interface but server-side backing code that supports that interface and the underlying database. Often that server-side backing code and database is tightly coupled with the user interface and "assumes" that the business processes currently implemented by the interface. Often, it is assumed that these business processes are static and rarely change. Unfortunately, that is often not the case. Developers react to the additional complexity in predictable ways.

Beware of undocumented developer assumptions. These assumptions are often made for developer convenience. Those assumptions make the new feature easier to code. I don't blame them; the extremely large feature set is often too difficult to keep in one person's head. Developers, as their assumptions produce initially more streamlined and easy to read code, believe that they are making a positive contribution.  Unfortunately, they are also inadvertently creating land-mines for other developers who aren't aware of those underlying assumptions and need to change that code for some other feature addition. As most of these assumptions go undocumented or even unidentified, other developers working on that section of code are often unaware of all the consequences of their changes.

Most monoliths don't measure feature usage. As a consequence, features, once implemented, never get removed. It would be logical to remove features that aren't used or aren't used often as the complexity for having that code remain will negatively impact your ability to add new features down the road. Sadly, getting budget for removing features is hard to do in most organizations as the benefit to doing so is hard to measure and too intangible.

 It's worth noting that often the design of the underlying database often becomes bloated along with the web application. Due to the large scope of business it supports, the underlying database often becomes an Achilles heel for the organization. Not only is database refactoring much more costly and difficult than refactoring code, it's not uncommon for that database to be used by multiple applications. The implication here is that change to the underlying database often has consequences for more than the monolithic application it was originally created for.

Data Store Strangle
Despite the length of time that databases (relational and non-relational) have existed, database design skills are woefully inadequate at most organizations. As a result, you often get a process-oriented database structure. That is, a database structure that is specific to processes currently implemented by the application that database supports. Usually, the mindset is that the database can be 'refactored' along with code as new features are implemented.

Database design impacts application code. At the risk of stating the obvious, applications have code dedicated to reading and writing to the database. Hopefully, database code is restricted to a data access layer within the application, but not all developers adopt the practice of separating data access concerns. However, the readability of that data access code is directly impacted by the quality of the design of the underlying database. If the database structure is complex to the point that its not understood, chances are the data access code that uses that database will be just as hard to understand. Furthermore, if the application uses a different domain mode than was implemented in the database, some type of data structure translation is present in data access code. The short story is that the database design used can greatly increase the size and complexity of data access code.

Refactoring databases is harder and more time-consuming than refactoring code. There are a couple of false assumptions buried in that line of thinking. First, it is assumed that refactoring a database is as easy as refactoring code. Not true. Any database refactoring often must take place while supporting existing code. Also, any database refactoring also must have some type of data conversion as a part of it.  Another way to look at it is that code only has state that's transient. That is, its state exists only at run time. Database state is persistent. Changes to that database must include converting the data that was stored into its new format.

Refactoring databases has more risk than refactoring code. If a code refactoring is discovered to have a defect, backing out that change is usually an option. Database refactoring changes usually don't have this safety net. Yes, you can restore a version of the database that was taken before the refactoring was implemented, but users will loose any data entered while that refactoring was in place. Unless you code and test a reverse conversion, implementing database refactoring changes commits you (pun definitely intended) past the point of no return. 

The Over-Engineered Tarball Component

Some developers can't resist over-engineering internal components they write. Often that happens because they are bored and looking for something more interesting to do. I can't fault them for that motivation. Unfortunately, when those mental musings get implemented in applications, additional complexity often results. This contributes to an application becoming a monolith.

I've seen a case where such a component was written to validate cash transfers from account to account. While laymen to this business process might look at such a transfer action as simple, those of you deeply embedded in bank know differently. The tax ramifications and considerations are incredible depending on how that transfer is recorded. In fact, we had over 100,000 permutations and combinations of transfers that needed to be verified and tested.  Anyway, the component in question utilized the same type of bit-switching algorithm that UNIX uses for file permissions, but with many more dimensions than just user, group, and all permissions.  Anyway, when that person left, nobody could understand the component nor could they effectively change the rules it applied.  Long story short: the application was held hostage by this embedded, but critical component.

Overly complex components have a way of creating complexity in the surrounding parts of the application. In my example, we had code to correct the validation verdict in places where it came up with the incorrect answer. This was needed as nobody knew how to change the overly-complex component handling validation.

The business process implemented by the component is often not understood. That is, replacing such a component is often more difficult as the business rules it implements are often not understood. It's hard to code a replacement without a specific target.

The Distributed Monolith
Separate services are supposed to increase developer velocity by reducing the size and complexity of code that needs to be changed. We all know that this doesn't always work out. We've all seen situations where feature additions require coordinated changes across multiple components. We've all seen that even though these services are theoretically decoupled and should be able to be changed/enhanced/deployed independently, that this doesn't seem to happen much of the time.  The reason is that these services are not truly decoupled. Let me explain.

Developers have a desire to consolidate code. We've incented this in developers for years by promoting DRY (Don't Repeat Yourself). Carrying this idea forward, developers consolidate repeated code whenever they find it. Invariably, business code gets consolidated into common libraries. It starts with implementing a service contract in code, making a library out of that, and sharing it across the publishing service and all consumers. It often progresses from there to include common business code that is listed as a dependency for multiple services. this common business code, which usually is used for manipulating common business data, tends to also be consolidated. Perhaps by publishing that logic in common libraries, and is used by several services.

Developers are often quite proud of the code consolidation. Unfortunately, whenever that common code changes, it forces a coordinated redeployment of all consumers along with the producer. In this world, there's no such thing as a non-breaking change. In essence, DRY, if used for business logic including service contracts, increases coupling between services. If that common code is purely technical and services are completely free to decide when they upgrade to new versions, then there's no coupling issue.

Distributed monoliths are caused by coupling between services that forces change across multiple services. In these cases, you're not getting the benefit of breaking the world up into smaller services. In fact, just the opposite. You get all the disadvantages of a highly coupled monolith along with the additional overhead of managing a larger number of services. In other words, you forgo all advantages of implementing microservices and pay more for the privilege.

Separate common business functionality into its own service instead. Deploy it once. call it from whatever services need it. Only maintain it once. 

Don't create common code that represents your service contracts. Consumers usually don't need all attributes in the request or response for a REST service call. Let's take an example of a REST call that returns customer information. For each customer, there might be 50 attributes. Of that, possibly a consumer only needs five or six. That consumer call should not break if new attributes are added or if an attribute that the consumer ignores is changed or withdrawn. If that contract is represented by common code, that common code will necessarily list all the attributes that the consumer ignores and such changes will be a breaking change. 

Services should always be able to choose when they upgrade included libraries.  Always - no exceptions. To get the benefits of breaking the monolith up into several services, you cannot permit any common code that could force a deployment of a service.

In forthcoming blog entries, I'll suggest tactics for breaking each of these types of monoliths into more manageable applications. Thanks for reading.

Saturday, August 27, 2016

A Pleasurable Journey into Text Translation using ANTLR4

For one of my clients, I needed to spec out a REST service API for developers. Although they had standardized on the API Blueprint product, I feel compelled to look for alternatives for specking out future REST APIs.  The input needed for API Blueprint is just too verbose and requires far too much time. If I expected to spend less time specking out REST APIs, this might not be an issue. Or at least not as much of an issue. I also checked out Swagger and RAML and found the same verbosity issue; I just don't have time for that on an ongoing basis.  There's a good review of these products that I found particularly useful here.

My thoughts turned to a syntax for specking REST APIs that would be far more streamlined and concise. Within an hour, I had a draft of a syntax (example below) that would be far less verbose and that I could increase my productivity significantly per API.  The problem would be writing something that could interpret specifications in this format and could then generate the verbose XML , Markdown, or YAML syntax for one of the other API designer products mentioned above. It turns out that there are products that specialize in text translation.  ANTLR is the most popular of these products right now.

The way ANTLR works is that you specify a grammar that describes the text you want to translate. ANTLR uses that grammar to generate Java code that can take text in that format and interpret it for you in the terms that you specified in the grammar. For instance, I define a REST Resource block in my grammar and told it about the syntax for different operations and the json arguments they accept as input or emit as output. ANTLR generated code will analyze the specifications I write and convert that to a Java object format that I can more easily read, interpret through code, and generate useful translated output with the help of a templating technology, such as Freemarker. An example grammar for the Java language as an example can be found here.

It turns out that specifying a grammar wasn't as easy as I thought it would be going in. What, in my mind, is a simple context is actually quite complex when you break it down into constructs that a product like ANTLR can understand. Essentially, you need to specify all whitespace (characters to ignore) and comments if the syntax is to support them. All special keywords and rules that govern when those keywords are expected to be used also need to be specified.  At this point, I should have just backed down from this idea and suffered through one of the more verbose solutions. However, by this point, I'm far too interested in how structured text gets specified and what's possible by interpreting it through code to stop.

I'm part way through this project and will open source it once complete.  For those taking similar text translating journeys with ANTLR, I have ferreted out some techniques that helped me immensely.

Write and test the Lexer portion of the grammar first.

ANTLR breaks up grammars into two pieces: a "Lexer" and a "Parser". A "lexer"  understands what characters and keywords are important for what you're doing and skipping any unneeded whitespace. It also formats those characters/keywords internally as "Tokens" so that it can be used for more sophisticated translation later on. A "Parser" applies rules to important characters and keywords to interpret context. For example, a REST resource definition doesn't make sense in the data type structure section of my proposed REST API specification syntax.

As the parser uses lexer output; it's important to make sure the lexer portion of your grammar tests out first. Any testing of the parser at this point is premature. Assertions in your lexer test should be:
  • Make sure all characters and keywords are recognized.
  • Make sure that the lexer identifies characters and keywords correctly. For instance, I had a bug early on where the keyword 'Resource' was recognized as a string literal. In my syntax, 'Resource' has a special context and meaning.

You can test the lexer generated from your grammer by iterating through the Tokens generated. Any unrecognized tokens shouild cause a test failure. If the lexer doesn't recognize your special characters and keywords (e.g. doesn't identify the correct number of keyword 'Resource' from your test sample), then it should also cause a test failure. 

Write Parser rules iteratively from general rules to more specific rules.

Parser rules apply context to the tokens identified by the Lexer. I found it much easier to start with very general parser rules and get those working. For example, my syntax has two main sections: a bounded context section that describes resources and operations and a Types section that describes all data types used by the API. My first iteration of the parser rules just identified the two sections.  That isn't enough to do what I need, but I didn't leave it there. Over time, I specified the portions of both sections and progressively describe them in more detail.

In other words, parser rules describe a section of your input text. The first test for parser rules can be simple; just test the start line/column position and end line/column position for each parser rule. If those are correct, then you can describe more specific rules that carves up the larger sections in the first iteration. Each parser rule you write has a value object specifically generated for it. That value object has the starting and ending token for the section it covers (you can get the starting and ending positions from those tokens).

There are a few points that aren't obvious about the ANTLR product to remember.
  • Lexer rules have UPPER_CASE names. Parser rules are lower case
  • At least one parser rule should apply to the entire document (minus skipped whitespace).
I'll post additional reports and publish the resulting work via Github when complete.  I'm still midway through this effort.

An Example REST API Specification Syntax

#student.spec - Student REST API
Bounded Context: Student Information {
Resource: Student // Everything about current and past students
Operation: /student - POST, json, student //Creates a student
httpStatus: 201,400
return: json, studentId
Operation: /student/{studentId} - PATCH, json, student //Update student (only those attributes provided)
httpStatus: 200,400,404,405
Operation: /student/{studentId} - DELETE //Delete student
httpStatus: 200,400,404,405
Operation: /student - GET //Finds a list of students by status
status - string[] // Status values to search by
httpStatus: 200,400
return: json, student
Operation: /student/{studentId} - GET //Finds a student by their id
httpStatus: 200,400,404
return: json, student
Types: {
student {
studentId - required, string // student Identifer that uniquely identifes a student
firstName - required, string $$Bill
middleName - string
lastName - required, string $$Williamson
title - enum{Mr, Ms, Mrs}
birthDate - required, date
primaryAddress - required, address
schoolAddress - address
primaryPhone - required, phone
cellPhone - phone
status - enum{Applied, Accepted, Active, NonActive}
address {
streetAddress1 - string $$123 Testing Lane
streetAddress2 - string
City - string $$Somewhere
StateCode - string(2) $$IL
zipCode - int(5)
zipCodeExt - int(4)
phone {
countryCode - int(2)
areaCode - int
prefix - int
line - int
extension - int
classSection {
title - string
discipline - string
courseNbr - int
building - string
room - string
time - string