If you want to get the entire contents of the file as a string one line at a time, you can use the clojure.java.io/reader method. The clojure.java.io/reader class creates a reader buffer, which is used to read each line of the file.
This may look very different from the kinds of databases you are used to working with. This design is sometimes referred to as "functional database", since it uses ideas from the domain of functional programming. The rest of the chapter describes how to implement such a database.
Time Series Database In One Line Of Clojure
If you've used a database system before, you are probably already familiar with the concept of an index, which is a supporting data structure that consumes extra space in order to decrease the average query time. In our database, an index is a three-leveled structure which stores the components of a datom in a specific order. Each index derives its name from the order it stores the datom's components in.
Before we can build complex querying facilities for our database, we need to provide a lower-level API that different parts of the system can use to retrieve the components we've built by their associated identifiers from any point in time. Consumers of the database can also use this API; however, it is more likely that they will be using the more fully-featured components built on top of it.
Since we treat our database just like any other value, each of these functions take a database as an argument. Each element is retrieved by its associated identifier, and optionally the timestamp of interest. This timestamp is used to find the corresponding layer that our lookup should be applied to.
Preparing an entity is done by calling the fix-new-entity function and its auxiliary functions next-id, next-ts and update-creation-ts. These latter two helper functions are responsible for finding the next timestamp of the database (done by next-ts), and updating the creation timestamp of the given entity (done by update-creation-ts). Updating the creation timestamp of an entity means going over the attributes of the entity and updating their :ts fields.
At this point we have the core functionality of the database in place, and it is time to add its raison d'être: insights extraction. The architecture approach we used here is to allow adding these capabilities as libraries, as different usages of the database would need different such mechanisms.
Finally, there's one thing that is still missing: a name. The only sensible option for an in-memory, index-optimized, query-supporting, library developer-friendly, time-aware functional database implemented in 360 lines of Clojure code is CircleDB.
Code churn is a powerful predictor of post-release defects, but it's real power lies in visualizing trends in your development projects. The tool we'll create in this article lets you reverse engineer your true development process from code. While the main purpose is to provide a short tutorial on Incanter's Zoo library, you'll also learn how to analyze time series in general.
There are a few tricky things to keep in mind. The first is that the construction function for a time-series-plot only accepts one data series. That's a problem since we want to visualize two trends: 1) the raw churn, and 2) its rolling average.
Now we just need to invoke as-time-series-plot to obtain our chart. The function takes three arguments. raw-churn and rolling-average are the values from the respective columns in our Zoo dataset. That's straightforward.
But the first argument, dates, requires your attention. We already have our time series in our dataset. We'll use it as X-axis but with one twist: Zoo requires us to convert our Joda time objects into milliseconds since the epoch. We express that conversion and data extraction in another function:
Our as-churn-chart function just calls the Java method getMillis on all time objects in our Zoo dataset (remember, a Zoo dataset is something with time objects in an :index column). We use Incanter's $ shortcut to select the data in the columns of interest. Everything is then fed to the as-time-series-plot function we defined above.
Time series data is a series of data points collected over time intervals for variables, giving us the ability to track changes over time. A time series is a time-oriented or chronological sequence of observations on a variable.
Because data points in time series are collected at various time periods, there is potential for correlation between observations. This is one of the features that distinguishes time series data from other data.
Consider if sensors send out data for every second and you own 10000 engines with each engine having 100 sensor values. This will result in storing 8 billion+ records in the database. Over a month and year, it becomes impossible to store such a high volume of data in a traditional database, and impossible to query such a database for a simple query such as fetch data for 10 sensors for a year. This is why the world is racing towards adopting time series databases for storing and retrieving data, for time series use cases and continuous data streams.
Relational database management systems (RDBMS) can be used to store and retrieve time series data. With the flexibility of RDBMSs, they can store the same data as a TSDB. The one key difference is that RDBMSs are not optimized for time series data and tend to be slower for inserting and retrieving time series data as data volumes keep growing as discussed in the example above.
Another type of database, NoSQL, is also often used to store time series data. Since NoSQL databases are more flexible in terms of the data format for each record, they are good for capturing time series data from a number of distinct sources. However, to query a NoSQL database means carefully examining the schema and writing a custom query against it. Complex operations such as different kinds of joins, which have benefited from decades of innovation on the SQL side, are likely to be slow and even buggy in the NoSQL camp.
Currently in the growing market of time series databases, InfluxDB stands out as promising an overall time series database. With good technical documentation it is easy to install, configure, and get started with InfluxDB. As it is a NoSQL-like database, we insert the data and we are good to go.
This way, we can perform queries using different clauses as per our needs and fetch the data in a time series format. As shown in the beginning, the same data can be used to plot a variety of charts using the data, which can be used to generate more insights and forecasting on that data. Further, this data can also be used for troubleshooting and understanding the stream of metrics and events.
In this article, I have tried to cover the what, why, and how of time series databases using InfluxDB as an example. As experts recognize the emerging need for a time series database, I recommend learning more about time series, as storing your projects in a time series format can be a gamechanger.
Welcome reader! This is a book about scripting with Clojure and babashka.Clojure is a functional, dynamic programming languagefrom the Lisp family which runs on the JVM. Babashka is a scripting environmentmade with Clojure, compiled to native with GraalVM. Theprimary benefits of using babashka for scripting compared to the JVM are faststartup time and low memory consumption. Babashka comes with batteries includedand packs libraries like clojure.tools.cli for parsing command line argumentsand cheshire for working with JSON. Moreover, it can be installed just bydownloading a self-contained binary.
In addition to clojure.core, the following libraries / namespaces are available in babashka.Some are available through pre-defined aliases in the user namespace,which can be handy for one-liners. If not all vars are available, theyare enumerated explicitly. If some important var is missing, an issue orPR is welcome.
We'll get to that. But first, what are they? Volatiles are mutable references, like atoms, refs, and agents. However, they do not impose any transactional disciplines as the others do. The only concession to concurrency is that volatiles force other threads to get fresh values of it every time.
Let's solve an actual problem using locks. If you have many threadsall printing to the console at the same time, very often you'll seethat the lines are mixed up. Two threads that print at exactly thesame time will send their characters at the same time, and the line isjust messed up.
Imagine you walk into a bank. You see a row of tellers and a line ofpeople waiting. How many bank transactions can happen at the sametime? Easy. It's the same as the number of tellers. The parallelismof the bank is how many things can happen at the exact sametime. If there are four tellers, four things can happen at the sametime.
Even though there are only four tellers, all of those clients waitingin line will be helped before the end of the day. They're basicallycompeting over the scarce resource of the teller's time. Butbecause of the concurrency system set up (the queue), they all knowthat their business will be handled eventually.
The immutable data structures. Clojure uses immutable data bydefault. Why is this important? Let's imagine a bank where you couldreuse checks. Let's write all the information in pencil. That couldwork, with a lot of discipline. Or you could make checks one-time-use(you create one, use it, then throw it away).
Or how about a bathroom shared by eight people with no lock! You cando it. Just be disciplined. Knock every time. What could go wrong?Start looking for a new place to live (or run to the hardware store tobuy a lock).
It sounds ridiculous, but that's basically what we do all the timewhen we use mutable data structures. You can still write concurrentsystems, but your job is harder and your success depends on disciplineinstead of easy rules like "wait your turn in line".
With a good spec, I don't even have to write example programs. Specs can also be used to generate example data, via the exercise fn. The examples below show exercising a couple of clj-xchart argument types, ::series/line-width and ::series/series-name: 2ff7e9595c
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