ffind v1.2.0 released!

The new version of ffind v1.2.0 is available in GitHub and PyPi. This version includes the ability to configure defaults by environment variables and to force case insensitivity in searches.

You can upgrade with

    pip install ffind --upgrade

This will be the latest version to support Python 2.6.

Happy searching!

ffind v1.0.2 released!

The new version of ffind (1.0.2) is available in GitHub and PyPi. This version includes the ability to execute python modules and scripts directly and some other minor improvements.

Happy developing!

All you need is cache

Cache is all you need
Cache is all you need

What is cache

More than a formal definition, I think that the best way of thinking about cache is an result from an operation (data) that gets saved (cached) for future use.

The cache value should be identifiable with a key that is reasonably small. This normally is the call name and the parameters, in some sort of hashed way.

A proper cache has the following three properties:

  1. The result is always replicable. The value can be scrapped without remorse.
  2. Obtaining the result from cache is faster than generate it.
  3. The same result will be used more than once.

The first property implies that the cache is never the True Source of Data. A cache that’s the True Source of Data is not a cache, it’s a database; and need be treated as such.

The second one implies that retrieving from cache is useful. If getting the result from the cache is slower (or only marginally better) than from the True Source of Data, the cache can (and should) be removed. A good candidate for cache should be a slow I/O operation or computationally expensive call. When in doubt, measure and compare.

The third property simply warns against storing values that will be used only once, so the cached value won’t be ever used again. For example, big parts of online games are uncacheable because they change so often they are read less times than written.

The simplest cache

The humblest cache is a well known technique called memoization, which is simply to store in process memory the results of a call, to serve it from there on the next calls with the same parameters. For example,

NUMBER = 100
def leonardo(number):

    if number in (0, 1):
        return 1

    return leonardo(number - 1) + leonardo(number - 2) + 1

for i in range(NUMBER):
    print('leonardo[{}] = {}'.format(i, leonardo(i)))  

This terribly performant code will return the first 100 Leonardo numbers. But each number will be calculated recursively, so storing the result we can greatly speed up the results. The key to store the results is simply the number.

cache = {}

def leonardo(number):

    if number in (0, 1):
        return 1

    if number not in cache:
        result = leonardo(number - 1) + leonardo(number - 2) + 1
        cache[number] = result

    return cache[number]

for i in range(NUMBER):
    print('leonardo[{}] = {}'.format(i, leonardo(i)))

Normally, though, we’d like to limit the total size of the cache, to avoid our program to run wild in memory. This restrict the size of the cache to only 10 elements, so we’ll need to delete values from the cache to allow new values to be cached:

def leonardo(number):

    if number in (0, 1):
        return 1

    if number not in cache:
        result = leonardo(number - 1) + leonardo(number - 2) + 1
        cache[number] = result

    ret_value = cache[number]

    while len(cache) > 10:
        # Maximum size allowed, 10 elements
        # this is extremely naive, but it's just an example
        key = cache.keys()[0]
        del cache[key]

    return ret_value

Of course, in this example every cached value never changes, which may not be the case. There’s further discussion about this issue below.

Cache keys

Cache keys deserve a small note. They are not usually complicated, but the key point is that they need to be unique. A non unique key, which may be produced by unproper hashing, will produce cache collisions, returning the wrong data. Be sure that this doesn’t happen.

Python support

Just for the sake of being useful, on Python3 there is support for a decorator to cache calls, so the previous code can look like this.

from functools import lru_cache

def leonardo(number):

    if number in (0, 1):
        return 1

    if number not in cache:
        result = leonardo(number - 1) + leonardo(number - 2) + 1
        cache[number] = result

    return cache[number]

so you can use it instead of implement your own.

The stereotypical web app cache

In the context of web apps, everyone normally thinks of memcached when thinks of cache.

Memcached will, in this stereotypical usage, use some allocated memory to cache database results or full HTML page, identified by an appropriate unique key, speeding up the whole operation. There are a lot of integrated tools with web frameworks and it can be clustered, increasing the total amount of memory and reliability of the system.typical_usage

In a production environment, with more than one server, the cache can  be shared among different servers, making the generation of content only happen once on the whole cluster, and then be able to be read by every consumer. Just be sure to ensure the first property, making possible to obtain the value from the True Source of Data at any point, from any server.

This is a fantastic setting, and worth using in services. Memcached can be also replaced by other tools like Redis, but the general operation is similar.

But there are more ways to cache!

Assuming a typical distributed deployment on a production web server, there are a lot of places where a cache can be introduced to speed up things.

This described service will have one DB (or a cluster) that contains the True Source of Data, several servers with a web server channeling requests to several backend workers, and a load balancer on top of that, as the entry point of the service.


Typically, the farther away that we introduce a cache from the True Source of Data, the less work we produce to the system and the most efficient the cache is.

Let’s describe possible caches from closest to the True Source of Data to farther away.

Cache inside the DataBase

(other than the internal cache of the database itself)

Some values can be stored directly on the database, derivating them from the True Source of Data, in a more manageable way.

A good example for that are periodic reports. If some data is produced during the day, and a report is generated every hour, that report can be stored on the database as well. Next accesses will be accessing the already-compiled report, which should be less expensive than crunching the numbers again.


Another useful way of caching values is to use replication. This can be supported by databases, making possible to read from different nodes at the same time, increasing throughput.

For example, using Master-Slave replication on MySQL, the True Source of Data is on the Master, but that information gets replicated to the slaves, that can be used to increase the read throughput.

Here the third property of cache shows up, as this is only useful if we read the data more often than we write it. Write throughput is not increased.

Cache in the Application Level

The juiciest part of a service is normally in this level, and here is where the most alternatives are available.

From the raw results of the database queries, to the completed HTML (or JSON, or any other format) resulting from the request, or any other meaningful intermediate result, here is where the application of caches can be most creative.

Memory caches can be set either internally per worker, per server, or  externally for intermediate values.

  • Cache per worker. This is the fastest option, as the overhead will be minimal, being internal memory of the process serving the requests. But it will be multiplied by the number of workers per box, and will need to be generated individually. No extra maintenance needs to be done, though.
  • External cache. An external service, like memcached. This will share all the cache among the whole service, but the delays in accessing the cache would be limited by the network. There is extra maintenance costs in setting the external service.
  • Cache per server. Intermediate. Normally, setting on each server a cache service like memcached. Local faster access shared among all workers on the same box, with the small overhead of using a protocol.

Another possibility worth noting in some cases is to cache in the hard drive, instead of RAM memory. Reading from local hard drive can be faster than accessing external services, in particular if the external service is very slow (like a connection to a external network) or if the data needs to be highly processed before being used. Hard drive caches can also be helpful for high volumes of data that won’t fit in memory, or reducing startup time, if starting a worker requires complex operations that produces a cacheable outcome.

Cache in the Web Server

Widely available web servers like  Apache or Nginx have integrated caches. This is typically less flexible than application layer caching, and needs to fit into common patterns, but it’s simple to setup and operate.

There’s also the possibility to return an empty response with status code 304 Not Modified, indicating that the data hasn’t changed since the last time the client requested the data. This can also be triggered from the application layer.

Static data should be, as much as possible, stored as a file and returned directly from the web server, as they are optimised for that use case. This allows the strategy of storing responses as static files and serve them through the web server. This, in an offline fashion, is the strategy behind static website generators like Nikola or Jekyll.

For sites that deal with huge number of requests that should return the same data, like online newspapers or Wikipedia, a cache server like Varnish can be set to cache them, that may be able to act as a load balancer as well. This level of cache may be done with the data already compressed in Gzip, for maximum performance.

Cache in the Client

Of course, the fastest requests is the one that doesn’t happen, so any information that can be stored in the client and avoid making a call at all will greatly speed an application. To achieve real responsiveness this needs to be greatly taken into account. This is a different issue than caching, but I translated an article a while ago about tips and tricks for improving user experience on web applications here.

The dreaded cache invalidation

The elephant in the room when talking about cache is “cache invalidation”. This can be an extremely difficult problem to solve in distributed environments, depending on the nature of the data.

The basic problem is very easy to describe: “What happens when the cache contains different data than the True Source of Data?

Some times this won’t be a problem. In the first example, the cached Leonardo numbers just can’t be different from the True Source of Data. If the value is cached, it will be the correct value. The same would happen with prime numbers, a calendar for 2016, or last month’s report. If the cached data is static, happy days.

But most of the data that we’d like to cache is not really static. Good data candidates for being cached are values that rarely change. For example, your Facebook friends, or your schedule for today. This is something that will be relatively static, but it can change (a friend can be added, a meeting cancelled). What would happen then?

The most basic approach is to refresh periodically the cache, like deleting the cached value after a predetermined time. This is very straightforward and normally supported natively by the cache tools, like allowing to store a value with a validation date. For example, assuming the user has a cached copy of the avatars of friends locally available, only ask again every 15 minutes. Sure, there will up to 15 minutes where a new avatar from a friend won’t be available, and the old one will be displayed, but that’s probably not a big deal.

On the other hand, the position on a leaderboard for a competitive video game, or the result on a live match in the World Cup is probably much more sensible for such a delay.

Even worse, we’ve seen that some options involve having more than one cache (cache per server, or per worker; or redundant copies for reliability purposes). If two caches contains different data, the user may be alternating between old and new data, which will be confusing at best and produce inconsistent results at worst.

This is a very real problem on applications working with eventually consistent databases (like the mentioned Master-Slave configuration). If a single operation involves writing a value, and then read the same value, the returned value could have a different value (the old one), potentially creating inconsistent results or corrupting the data. Two very close operations modifying the same data by two users could also produce this effect.

Periodically refreshing the cache can also produce bad effects in production environment, like synchronising all the refresh happening at the same time. This is typical in systems that refresh data for the day at exactly 00:00. At exactly that time all workers will try to refresh all the data at the same time, orchestrating a perfectly coordinated distributed attack against the True Source of Data. It is better to avoid perfectly round numbers and use some randomness instead, or set numbers relative to the last time the data was requested from the True Source of Data, avoiding synchronised access.

This avalanche effect can also happen when the cache cluster changes (like adding or removing nodes, for example, when one node fails). These operations can invalidate or make unavailable high numbers of cached content, producing an avalanche of requests to the True Source of Data. There are techniques to mitigate this, like Consistent Hash Rings, but they can be a nightmare if faced in production.

Manually invalidating the cache when the data changes in the True Source of Data is a valid strategy, but it needs to invalidate the results from all the caches, which is normally only feasible for external cache services. You simply can’t access the internal memory of a worker on a different server. Also,  depending on the rate of invalidation per read cached value, can be counter productive, as it will produce an overhead of calls to the cache services. It also normally requires more development work, as this needs a better knowledge of the data flow and when the value in the cache is no longer valid. Sometimes that’s very subtle and not evident at all.


Caching is an incredibly powerful tool to improve performance in software systems.  But it can also be a huge pain due all those subtle issues.

So, some tips to deal with cache

  • Understand the data an how it’s consumed by the user. A value that changes more often than gets read it’s not a good cache candidate.
  • Ensure the system has a proper cache cycle. At the very least, understand how cache flows and what are the implications of cache failure.
  • There are a lot of ways and levels to cache. Use the most adequate to make caching efficient.
  • Cache invalidation can be very difficult. Sorry about that.

ffind v0.8 released

Good news everyone!

The new version of find (0.8) is available in GitHub and PyPi. This version includes performance improvements, man page and fuzzy search support.


Future as a developer and the ever changing picture

A few weeks ago I came by a couple of articles my Marco Arment that share the theme of the current status of accelerated change within the development community as a way of stressing up, and being difficult to be up to date. After all, one gets tired of learning a new framework or language every size months. It gets to a point where is not funny or interesting anymore.

It seems like two different options are presented, that are available for developers after some time:

  • Keep up, meaning that you adopt rapidly each new technology
  • Move to other areas, typically management

Both are totally valid options, as I already said in this blog that I don’t like when good developers move to different areas (to me it’s sort of a surgeon deciding she had enough after a few years and move to manage the hospital). Though, obviously each person has absolutely every right to choose their career path.

But I think that it’s all mostly based in an biased and incorrect view of the field of technology and the real pace of changes.

In the last years, there has been an explosion of technologies, in particular for web. Ruby on Rails almost feels introduced at the same time as COBOL. NodeJS seemed to be in fashion for a while. The same with MongoDB or jQuery.

We all know that being stressed is not a great way of learn
We all know that being stressed is not a great way of learn

In the last 6 or 7 years there has been an incredible explosion in terms of open source fragmentation. Probably because GitHub (and other online repos) and the increase in communication through the Internet, the bar to create a web framework and offer it to the world has been lowered so much, that a lot of projects that would’ve been not exposed previously, has gotten more exposure. As a general effect, is positive, but it came with the negative effect that every year there is a revolution in terms of technologies, which forces everyone to catch up and learn the brand new tool that is the best for the current development, increasing the churning of buzz words.

But all this is nothing but an illusion. We developers tend to laugh at the common “minimum 3+ years of experience in Swift”, but we still get the notion that we should be experts in a particular language, DB or framework since day one. Of course, of the one on demand today, or we are just outdated, dinosaurs that should retire.

Software development is a young field, full of young people. That’s great in a lot of aspects, but we need to appreciate experience, even if it comes from using a different technology. It doesn’t look like it, but there’s still a lot of projects done in “not-so-fancy” technologies. That includes really old stuff like Fortran or COBOL, but also C++, Java, Perl, PHP or Ruby.

Technologies gets established by a combination of features, maturity, community and a little luck.  But once they are established, they’re quite resilient and don’t go away easily.  They are useful for quite a long time. Right now it’s not that difficult to pick a tool that is almost guaranteed to be around in the next 10-15 years. Also, most of the real important stuff is totally technology agnostic, things like write clean code, structure, debug ability, communication, team work, transform abstract ideas into concrete implementations, etc… That simply does not go away.

Think about this. iOS development started in 2008. Smartphones are radically different beasts than the ones available 6 years ago, probably the environment that has changed more. The basics are the same, though. And even if Swift has been introduced this year, it’s based in the same principles. Every year there has been tweaks, changing APIs, new functionalities. But the basic ideas are still the same. Today a new web development using LAMP is totally viable. Video games still relay on C++ and OpenGL. Java is still heavily used. I use all the time ideas mainly developed in the 70s like UNIX command line or Vim.

Just because every day we get tons of news about new startups setting up applications on new paradigms, that doesn’t mean that they don’t coexist with “older” technologies.

Of course, there are new tricks to learn, but it’s a day by day additive effort. Real revolution and change of paradigm is rare, and normally not a good sign. Changing from MySQL to PostgreSQL shouldn’t be considered a major change in career. Searching certain stability in the tools you use should be seen as good move.

We developers love to stress the part of learning everyday something new and constantly challenge ourselves, but that should be taken also in perspective with allowing time to breathe. We’ve created a lot of pressure on ourselves in terms of having to be constantly pushing with new ideas, investigating in side projects and devoting ourselves 100% of the time to software. That’s not only not realistic. It’s not good.

You only have to breathe.  And just worry on doing a good work and enjoy learning.

The amazing forgiveness of software

One of the things I like most about developing software is the fact that you can recover from most mistakes with very few long term impact.

Bugs are unavoidable, and most of the people involved on programming deeply understands that is something we all live with.  So,  there’s no hard feelings, once you find a bug, you fix it and immediately move on. Not only no one thinks that you’re a bad developer because you write bugs, but typically the impact of a bug is not that problematic.

Yes, there are some bugs that are just terrible. And there’s always the risk of losing data or do some catastrophic operation on production. But those are comparatively rare, and with the proper practices, the risk and damage can be reduced. Most on the day to day operation involves mistakes that have a much limited effect. Software development is more about being bold and move fast fixing your mess, than it is to play safe (within limits, of course).

This can affect production data! Show warning sign!
This can affect production data! Display warning sign!

Because the greatness of software is that you can break it on purpose and watch it explode, and then fix that problem. In a controlled environment. Without having to worry about permanent effects or high costs. And a good test is the one that ambushes the code and try to viciously stab it with a poisonous dagger. The one that can hurt. So you know that your system is strong enough against that attack. And then iterate. Quickly. It’s like having a permanent second chance to try again.

Not every aspect of live is that forgiving. I guess that a lot of doctors would love to be able to do the same.

Do you mind if we start the trial again and the jury forgets everything, your honor?
Do you mind if the jury forgets everything and we start the trial again, your honor?

Agile important bits

There is a lot of Agile talking and I think it has reached a point where it is, if not standard, at least a common way of doing software. But, even if there is a lot talking about Agile methodologies, and companies telling that the are doing Agile, are they really doing it? I’m not so sure. When relating to Agile, I always come back to the source, which is the Agile manifesto. I really like its simplicity. Let me copy it here

We are uncovering better ways of developing software by doing it and helping others do it.Through this work we have come to value:

Individuals and interactions over processes and tools

Working software over comprehensive documentation

Customer collaboration over contract negotiation

Responding to change over following a plan

That is, while there is value in the items on the right, we value the items on the left more.

The first time I read it, I must say I wasn’t impressed. Yeah, sure… Great values, dude. But after spending more time, I come to see that as a really good set of values that, in mi opinion, work really well for software development. In practice, I think there are some of those values that are somehow “forgotten” over the day to day operations. I’ve been thinking about what are the ideas that, again in my opinion, are the ones I consider the best way of implementing those values. Consider them my personal “Agile highlighted parts”

Never-ending learn

Well, maybe not in 21 days, but that's the spirit...
Well, maybe not in 21 days, but that’s the spirit…

In my opinion, the single word that is capital in software development is “LEARN”. The same thing applies to Agile. Agile is about being constantly learning. Change (which is also a very important word) is just a consequence of this learning process, the outcome. Because we learn how to do things better, we change our way to work. Because we truly understand the problems of the customer, we can develop what the customer needs (which may not be what the customer has in mind in the first place). This learning process should not view restricted to the developers. It is also applicable to the rest of the people involved, including the customer. If the customer is not willing to learn in the process and refuses to accept feedback, then the process is much more difficult. That could be the single most important risk in Agile, as that will make the collaboration difficult and adds a lot of friction. Even in products aimed for mass consumption, this process can be present one way or another. For example, recently there has been a lot of discussion about iOS 7, and how consumers have learned how to use a touch screen and elements that were present on previous versions to help usage are no longer needed as average consumer know now what it is about. Refusal of learning can also be a problem in Agile. There are people that do not like the constant effort and the change in mindset that it implies.

Team centric view.

The team in Agile is king. When I say “a team” I am not referring to a list of people put together on the same building working on the same product. A team is much more than that. It is people working towards the same goal, but also effectively working close together and helping each other. Just as a soccer team needs players with different abilities, so does software teams. The people working on the same product will have to be, with high probability, multidisciplinary. That has not been usually the case on software companies, where you have the testing department in one side, the design department in another, and the R&D department detached from the rest of teams, communicating through big documents and formal meetings. That creates the need of strict interfaces, and negotiation between the parts to agree how to exchange information.

Some development talk is full of wibbly-wobbly... timey-wimey... stuff
Most development talk is full of wibbly-wobbly… timey-wimey… stuff

Instead, a team will try to learn and improve how to work and exchange information. Asking constantly, “how can I make the work of X easier?”. To be able to do that, you have to know and understand what are the problems that X will face, and the only way of knowing that is to work closely with them. That is more difficult that it looks. We developers are usually very “machine centric”, tending to try to fix everything with a script, or adding a new feature that complicates the system. It is not simple to learn about how and why other “non-techies” people (sales, designers, etc) are doing the things they are doing the way they are doing them. We prefer to keep running out scripts and our UNIX commands. We talk our techie talk of threads, classes, recursion and functional programming. But the learning of the”domain knowledge” is really what makes the difference between a good team and a great one. And that knowledge can only be achieved constantly learning from one another. Both the developers understanding the real problems of the customer, but also, in some cases, the customer understanding how the team works and what is possible on a certain amount of time. Creating a good team is very important and challenging. But also accepting that it is formed by different individuals, with different capabilities, strong and weak points, is probably one of the most difficult parts in any organisation. I always think that the most challenging task is to deal with people, which, no matter how “good cultural fit” is achieved, will be individuals different from any other one. Acknowledging it and being able to make everyone on the same page is difficult, but capital for highly successful teams.

Interact in a constant, but structured fashion

While interaction is really important, some balance need to be achieved with interrupting ongoing work. Agile is not about “hey, we can change all at any point”. I know, the name is a little misleading. It about knowing that you are going to change stuff, and deciding what at certain points. One of the details that I found out more efficient in that are sprints and stand up meetings. Both are tools to structure the conversation while providing constant feedback.

Stand up meetings

Doing stand up meetings in the best way is more difficult that it looks. It needs to be kept very simple, fast, and with as little noise as possible. Basically each participant need to say, keeping it simple: What did I do yesterday? What I am going to do today? Are there any problems in the way? And listen carefully to the rest of the people to be aware of the work being done on the team, and to see if you can give support to anyone (during the meeting or later, if more than a few minutes are necessary). Being actually standing up and in a different room helps, as you tend to keep things up to the point, eliminating distractions. One interesting part is not the meeting itself, but the time that previously is used to structure your mind into what you’re going to say. That helps a lot in planning and in keeping you focused, as everyday you’ll have to explain what have you done. Focus is paramount in software development. Another important thing is to keep that as a strong habit. There are always moments when it looks like no one is saying something new, and it feel as redundant. If the meeting is all about the same things for a long time, then is clearly a problem (your tasks are not as small as they should). But the benefits in terms of constant feedback and focus are not achieved until some time. I also think that managers/product owners should be present on the meeting, and probably participate actively. Remember, it is part of the learning process and it’s a very good opportunity to show how the team is working and what are the management tasks. A proper star up meeting reduces the need to interrupt the work of the day with typical “what are you working on?” questions, and detects very fast problems and blockers. It structures communication to channel it.


Well, it does not scream "sustainable pace", but still...
Well, it does not scream “sustainable pace”, but still…

While the standup meetings structures the communication between the team, Sprints structure the communication between the team and “the external world”. The idea of the sprint is to produce something that can be shown, and ideally works, even if it’s small and not complete. That gives feedback and then the goal for the next sprint can be decided. While having a simple objective for a sprint can be good, I don’t think is necessary. A simple grouping of tasks with no particular meaning together may well be the objective. I don’t like the word “Sprint”. I think is not fortunate, as it will not give the idea a long term race, but a strong burst in effort. Software projects are long, and teams should find a comfortable development speed, or the quality of the result will suffer. I’d prefer something like “stage” (using bicycle race metaphor), because the main idea is that they should have a sustainable pace. The sprint objective is not a contract signed by blood of your firstborn. It is a reasonable objective that the team honestly thinks can be achieved.  Let me get back to  couple of ideas there. What can be achieved on a sprint is something that can only be decided by the team. They are the only ones with the knowledge of what is possible and what is not. Estimations are that, assumptions. A typical problem in software development is unrealistic deadlines by management, that will be used as weapons against the developers, and the natural response from developers is to produce “safe” estimations, much bigger that they should, so they can be protected. This is not a good thing, and it is a reflection of distrust. The way to overcome that is to treat estimations as that, and try to improve them, without punishment for  mistakes. Let me rephrase it: Estimations will be wrong. Often. The objective should be to improve them, and to not be wrong by a huge margin. Dividing work in small tasks will help, but that’s an inherent problem.

Make it work

I consider myself a pragmatist. At the end, everything should be done not for the sake of it, but because it helps towards an end. As with a lot of good ideas, people has fallen too often in “Agile cargo cult”, just following processes without thinking why, or without analysing who are the people on charge of doing it. Again, analysing and learning what’s going on is important, to enable the best concepts applied to a particular team.