A little while ago, I started learning Kotlin, and I have done its basics, variables, classes, lists, and arrays, etc. but the book I was learning from seemed to miss one important aspect, reading and writing to a file, maybe a function like "fwrite" in C++
So I searched google, and yes, reading and writing bytes were easy enough. However, I being used to C++'s open personality, wanted to make a "kind of" database.
In C++ I would simply make a struct and keep appending it to a file, and then read all the stored objects one by one, by placing "fread" in a for loop or just reading into an array of the struct in one go, as the struct was simply just the bytes allocated to the variables inside it.
However in Kotlin, there is no struct, instead, we use Data Class to group data. I was hoping there was an equally easy way to store data in a file in form of Data Class and read it into maybe a List of that class, or if that is not possible, maybe some other way to store grouped data that would be easy to read and write.
Easiest way is to use a serialization library. Kotlin already provides something for that
TL;DR;
Add KotlinX Serialization to your project, choose the serialization format you prefer (protobuf or cbor will fit, go for json if you prefer something more human readable although bigger in size), use the proper serializer for generating your ByteArray and write it to a file using Kotlin methods for that
Generating the ByteArray might be tricky, not sure as I'm telling this from memory. What I can tell for sure is that if you choose JSON you can get the string representation and write to a file. So I'm assuming the same will be valid for binary formats (but writing to a file in binary instead of strings)
What you need can be fulfilled by ROOM DATABASE
It is officially recommended by GOOGLE, It uses your Android application's internal Database which is made using SQLITE
You can read more info about ROOM at
https://developer.android.com/jetpack/androidx/releases/room?gclid=Cj0KCQjw5ZSWBhCVARIsALERCvwjmJqiRPAnSYjhOzhPXg8dJEYnqKVgcRxSmHRKoyCpnWAPQIWD4gAaAlBnEALw_wcB&gclsrc=aw.ds
It provided Data Object Class (DAO) and Entity Classes through which one can access the database TABLE using SQL Queries.
Also, it will check your queries at compile time for any errors in it.
Note: You need to have basic SQL Knowledge for building the queries for CRUD Operations
I have some doubts. I'm doing a BI for my company, and I needed to develop a data converter in ETL because the database to which it's connected (PostgreSQL) is bringing me some negative values within the time CSV. It doesn't make much sense to bring from the database (to which we don't have many accesses) negative data like this:
The solution I found so that I don't need to rely exclusively on dealing directly with the database would be to perform a conversion within the cloudconnect. I verified in my researches that the one that most contemplates would be the normalizer, but there are not many explanations available. Could you give me a hand? because I couldn't parameterize how I could convert this data from 00:00:-50 to 00:00:50 with the normalizer.
It might help you to review our CC documentation: https://help.gooddata.com/cloudconnect/manual/normalizer.html
However, I am not sure if normalizer would be able to process timestamps.
Normalizer is basically a generic transform component with a normalization template. You might as well use reformat component that is more universal.
However, what you are trying to do would require some very custom transform script, written in CTL (CloudConnect transformation language) or java.
You can find some templates and examples in the documentation: https://help.gooddata.com/cloudconnect/manual/ctl-templates-for-transformers.html
This question tries to collect information spread over questions about different languages and YAML implementations in a mostly language-agnostic manner.
Suppose I have a YAML file like this:
first:
- foo: {a: "b"}
- "bar": [1, 2, 3]
second: | # some comment
some long block scalar value
I want to load this file into an native data structure, possibly change or add some values, and dump it again. However, when I dump it, the original formatting is not preserved:
The scalars are formatted differently, e.g. "b" loses its quotation marks, the value of second is not a literal block scalar anymore, etc.
The collections are formatted differently, e.g. the mapping value of foo is written in block style instead of the given flow style, similarly the sequence value of "bar" is written in block style
The order of mapping keys (e.g. first/second) changes
The comment is gone
The indentation level differs, e.g. the items in first are not indented anymore.
How can I preserve the formatting of the original file?
Preface: Throughout this answer, I mention some popular YAML implementations. Those mentions are never exhaustive since I do not know all YAML implementations out there.
I will use YAML terms for data structures: Atomic text content (even numbers) is a scalar. Item sequences, known elsewhere as arrays or lists, are sequences. A collection of key-value pairs, known elsewhere as dictionary or hash, is a mapping.
If you are using Python, using ruamel will help you preserve quite some formatting since it implements round-tripping up to native structures. However, it isn't perfect and cannot preserve all formatting.
Background
The process of loading YAML is also a process of losing information. Let's have a look at the process of loading/dumping YAML, as given in the spec:
When you are loading a YAML file, you are executing some or all of the steps in the Load direction, starting at the Presentation (Character Stream). YAML implementations usually promote their most high-level APIs, which load the YAML file all the way to Native (Data Structure). This is true for most common YAML implementations, e.g. PyYAML/ruamel, SnakeYAML, go-yaml, and Ruby's YAML module. Other implementations, such as libyaml and yaml-cpp, only provide deserialization up to the Representation (Node Graph), possibly due to restrictions of their implementation languages (loading into native data structures requires either compile-time or runtime reflection on types).
The important information for us is what is contained in those boxes. Each box mentions information which is not available anymore in the box left to it. So this means that styles and comments, according to the YAML specification, are only present in the actual YAML file content, but are discarded as soon as the YAML file is parsed. For you, this means that once you have loaded a YAML file to a native data structure, all information about how it originally looked in the input file is gone. Which means that when you dump the data, the YAML implementation chooses a representation it deems useful for your data. Some implementations let you give general hints/options, e.g. that all scalars should be quoted, but that doesn't help you restore the original formatting.
Thankfully, this diagram only describes the logical process of loading YAML; a conforming YAML implementation does not need to slavishly conform to it. Most implementations actually preserve data longer than they need to. This is true for PyYAML/ruamel, SnakeYAML, go-yaml, yaml-cpp, libyaml and others. In all these implementations, the style of scalars, sequences and mappings is remembered up until the Representation (Node Graph) level.
On the other hand, comments are discarded rather early since they do not belong to an event or node (the exceptions here is ruamel which links comments to the following event, and go-yaml which remembers comments before, at and after the line that created a node). Some YAML implementations (libyaml, SnakeYAML) provide access to a token stream which is even more low-level than the Event Tree. This token stream does contain comments, however it is only usable for doing things like syntax highlighting, since the APIs do not contain methods for consuming the token stream again.
So what to do?
Loading & Dumping
If you need to only load your YAML file and then dump it again, use one of the lower-level APIs of your implementation to only load the YAML up until the Representation (Node Graph) or Serialization (Event Tree) level. The API functions to search for are compose/parse and serialize/present respectively.
It is preferable to use the Event Tree instead of the Node Graph as some implementations already forget the original order of mapping keys (due to internally using hashmaps) when composing. This question, for example, details loading / dumping events with SnakeYAML.
Information that is already lost in the event stream of your implementation, for example comments in most implementations, is impossible to preserve. Also impossible to preserve is scalar layout, like in this example:
"1 \x2B 1"
This loads as string "1 + 1" after resolving the escape sequence. Even in the event stream, the information about the escape sequence has already been lost in all implementations I know. The event only remembers that it was a double-quoted scalar, so writing it back will result in:
"1 + 1"
Similarly, a folded block scalar (starting with >) will usually not remember where line breaks in the original input have been folded into space characters.
To sum up, loading to the Event Tree and dumping again will usually preserve:
Style: unquoted/quoted/block scalars, flow/block collections (sequences & mappings)
Order of keys in mappings
YAML tags and anchors
You will usually lose:
Information about escape sequences and line breaks in flow scalars
Indentation and non-content spacing
Comments – unless the implementation specifically supports putting them in events and/or nodes
If you use the Node Graph instead of the Event Tree, you will likely lose anchor representations (i.e. that &foo may be written out as &a later with all aliases referring to it using *a instead of *foo). You might also lose key order in mappings. Some APIs, like go-yaml, don't provide access to the Event Tree, so you have no choice but to use the Node Graph instead.
Modifying Data
If you want to modify data and still preserve what you can of the original formatting, you need to manipulate your data without loading it to a native structure. This usually means that you operate on YAML scalars, sequences and mappings, instead of strings, numbers, lists or whatever structures the target programming language provides.
You have the option to either process the Event Tree or the Node Graph (assuming your API gives you access to it). Which one is better usually depends on what you want to do:
The Event Tree is usually provided as stream of events. It may be better for large data since you do not need to load the complete data in memory; instead you inspect each event, track your position in the input structure, and place your modifications accordingly. The answer to this question shows how to append items giving a path and a value to a given YAML file with PyYAML's event API.
The Node Graph is better for highly structured data. If you use anchors and aliases, they will be resolved there but you will probably lose information about their names (as explained above). Unlike with events, where you need to track the current position yourself, the data is presented as complete graph here, and you can just descend into the relevant sections.
In any case, you need to know a bit about YAML type resolution to work with the given data correctly. When you load a YAML file into a declared native structure (typical in languages with a static type system, e.g. Java or Go), the YAML processor will map the YAML structure to the target type if that's possible. However, if no target type is given (typical in scripting languages like Python or Ruby, but also possible in Java), types are deduced from node content and style.
Since we are not working with native loading because we need to preserve formatting information, this type resolution will not be executed. However, you need to know how it works in two cases:
When you need to decide on the type of a scalar node or event, e.g. you have a scalar with content 42 and need to know whether that is a string or integer.
When you need to create a new event or node that should later be loaded as a specific type. E.g. if you create a scalar containing 42, you might want to control whether that it is loaded as integer 42 or string "42" later.
I won't discuss all the details here; in most cases, it suffices to know that if a string is encoded as a scalar but looks like something else (e.g. a number), you should use a quoted scalar.
Depending on your implementation, you may come in touch with YAML tags. Seldom used in YAML files (they look like e.g. !!str, !!map, !!int and so on), they contain type information about a node which can be used in collections with heterogeneous data. More importantly, YAML defines that all nodes without an explicit tag will be assigned one as part of type resolution. This may or may not have already happened at the Node Graph level. So in your node data, you may see a node's tag even when the original node does not have one.
Tags starting with two exclamation marks are actually shorthands, e.g. !!str is a shorthand for tag:yaml.org,2002:str. You may see either in your data, since implementations handle them quite differently.
Important for you is that when you create a node or event, you may be able and may also need to assign a tag. If you don't want the output to contain an explicit tag, use the non-specific tags ! for non-plain scalars and ? for everything else on event level. On node level, consult your implementation's documentation about whether you need to supply resolved tags. If not, same rule for the non-specific tags applies. If the documentation does not mention it (few do), try it out.
So to sum up: You modify data by loading either the Event Tree or the Node Graph, you add, delete or modify events or nodes in the data you get, and then you present the modified data as YAML again. Depending on what you want to do, it may help you to create the data you want to add to your YAML file as native structure, serialize it to YAML and then load it again as Node Graph or Event Tree. From there, you can include it in the structure of the YAML file you want to modify.
Conclusion / TL;DR
YAML has not been designed for this task. In fact, it has been defined as a serialization language, assuming that your data is authored as native data structures in some programming language and from there dumped to YAML. However, in reality, YAML is used a lot for configuration, meaning that you typically write YAML by hand and then load it into native data structures.
This contrast is the reason why it is so difficult to modify YAML files while preserving formatting: The YAML format has been designed as transient data format, to be written by one application, and then to be loaded by another (or the same) application. In that process, preserving formatting does not matter. It does, however, for data that is checked-in to version control (you want your diff to only contain the line(s) with data you actually changed), and other situations where you write your YAML by hand, because you want to keep style consistent.
There is no perfect solution for changing exactly one data item in a given YAML file and leaving everything else intact. Loading a YAML file does not give you a view of the YAML file, it gives you the content it describes. Therefore, everything that is not part of the described content – most importantly, comments and whitespace – is extremely hard to preserve.
If format preservation is important to you and you can't live with the compromises made by the suggestions in this answer, YAML is not the right tool for you.
I would like to challenge the accepted answer. Whether you can preserve comments, the order of map keys, or other features depends on the YAML parsing library that you use. For starters, the library needs to give you access to the parsed YAML as a YAML Document, which is a collection of YAML nodes. These nodes can contain metadata besides the actual key/value pairs. The kinds of metadata that your library chooses to store will determine how much of the initial YAML document you can preserve. I will not speak for all languages and all libraries, but Golang's most popular YAML parsing library, go-yaml supports parsing YAML into a YAML document tree and serializing YAML document back, and preserves:
comments
the order of keys
anchors and aliases
scalar blocks
However, it does not preserve indentation, insignificant whitespace, and some other minor things. On the plus side, it allows modifying the YAML document and there's another library,
yaml-jsonpath that simplifies browsing the YAML node tree. Example:
import (
"github.com/stretchr/testify/assert"
"gopkg.in/yaml.v3"
"testing"
)
func Test1(t *testing.T) {
var n yaml.Node
y := []byte(`# Comment
t: &t
- x: 1 # anchor
a:
b: *t # alias
b: |
cccc
dddd
`)
err := yaml.Unmarshal(y, &n)
assert.NoError(t, err)
y2, _ := yaml.Marshal(&n)
assert.Equal(t, y, y2)
}
I hope it’s Ok to post a complete naive question here for LO or OO experts.
I’m looking for advice on whether scripting LibreOffice or OpenOffice would be suitable for the following:
General Question
I’m looking to generate PDF reports, based on a combination of a “template” and a set of data (currently in JSON format) and inserted images.
This would act as a headless service that gets invoked when necessary from a web server, when a user requests a PDF report (on linux).
We have a need to frequently modify/customise/generate new templates, hence the reluctance to go down a route of using something like Reportlab (plus I don't know Reportlab at all, so face huge learning curve that way
Background
This is in contrast to using an approach of using a PDF library like Reportlab directly within the web server, and having to build up the template/report programmatically.
As LibreOffice/OpenOffice is obviously a lot faster for generating good looking report "templates", this is a question about doing both the template generation, plus final template + data -> PDF generation all directly within LibreOffice.
Some more specifics
The data values would mostly either be substituted into fields in the template, with no to minimal processing of values required.
However, there would be situations where some of the data is in “sets” that would be shown in a table type view, and the number of fields (and so number of table rows for instance) would need to vary per report, based on the number of values in that particular JSON data.
Additionally, I’d need to be able to include (“import”) images into the report. Some of the JSON data would be paths to image files, and I’d like to include those. Again for these, the number of image may vary between each report.
This wouldn't be high frequency at all, so would not need to run either LO/OO as a service, but could simply invoke when required with a sys call. Conceptually something like "LibreOffice --template 'make_fancy.report' <data.json> <output_file.pdf>"
If this approach would be reasonable in either LO or OO, what languages are best to script in? (Hopefully python3).
I know that there are a lot of packages around which allow you to create or read e.g. PDF, Word and other files.
What I'm interested in (and never learned at the university) is how you create such a package? Are you always relying on source code being given by the original company (such as Adobe or Microsoft), or is there another clever way of working around it? Should I analyze the individual bytes I see in e.g. PDF files?
It varies.
Some companies provide an SDK ("Software Development Kit") for their own data format, others only a specification (i.e., Adobe for PDF, Microsoft for Word and it's up to the software developer to make sure to write a correct implementation.
Since that can be a lot of work – the PDF specification, for example, runs to over 700 pages and doesn't go deep into practically required material such as LZW, JPEG/JPEG2000, color theory, and math transformations – and you need a huge set of data to test against, it's way easier to use the work that others have done on it.
If you are interested in writing a support library for a certain file format which
is not legally protected,
has no, or only sparse (official) documentation,
and is not already under deconstruction elsewhere,a
then yes: you need to
gather as many possible different files;
from as many possible sources;
(ideally, you should have at least one program that can both read and create the files)
inspect them on the byte level;
create a 'reader' which works on all of the test files;
if possible, interesting, and/or required, create a 'writer' that can create a new file in that format from scratch or can convert data in another format to this one.
There is 'cleverness' involved, mainly in #3, as you need to be very well versed in how data representation works in general. You should be able to tell code from data, and string data from floating point, and UTF8 encoded strings from MacRoman-encoded strings (and so on).
I've done this a couple of times, primarily to inspect the data of various games, mainly because it's huge fun! (Fair warning: it can also be incredibly frustrating.) See Reverse Engineering's Reverse engineering file containing sprites for an example approach; notably, at the bottom of my answer in there I admit defeat and start using the phrases "possibly" and "may" and "probably", which is an indication I did not get any further on that.
a Not necessarily of course. You can cooperate with other whose expertise lies elsewhere, or even do "grunt work" for existing projects – finding out and codifying fairly trivial subcases.
There are also advantages of working independently on existing projects. For example, with the experience of my own PDF reader (written from scratch), I was able to point out a bug in PDFBox.