Storing 6 billion floats for easy access in files - php

I need to save 250 data files an hour with 36000 small arrays of [date, float, float, float] in python, that I can read somewhat easily with PHP. This needs to run for 10 years minimum, on 6tb of storage.
What is the best way to save these individual files, I am thinking python struct. But it starts to look bad for the job with large data amounts?
example of data
a = [["2016:04:03 20:30:00", 3.423, 2.123, -23.243], ["2016:23:.....], ......]
Edit:
Space, is more important than unpacking speed and computation. Since the space is very limiting.

So you have 250 data providers of some kind, which are providing 10 samples per second of (float, float, float).
Since you didn't specify what your limitations are, there are more options.
Binary files
You could write files of fixed array of 3*36000 floats with struct, at 4 bytes each gets you at 432.000 bytes per file. You can encode the hour in the directory name and id of the data provider in file name.
If your data isn't too random, a decent compression algorithm should shave enough bytes, but you would probably need to have some sort of delayed compression if you wouldn't want to lose data.
numpy
An alternative to packing with struct is numpy.tofile, which stores the array directly to file. It is fast, but always stores data in C format, where you should take care if the endian is on target machine is different. With numpy.savez_compressed you can store a number of arrays in one npz archive, and also compress it at same time.
JSON, XML, CSV
A good option is any of the mentioned formats. Also worth mentioning is JSON-lines format, where each line is a JSON encoded record. This is to enable streaming writing, where you keep a valid file format after each write.
They are simple to read, and the syntactic overhead goes away with compression. Just don't do string concatenation, use a real serializer library.
(SQL) Database
Seriously, why not use a real database?
Obviously you will need to do something with the data. With 10 samples per second, no human will need so much data, so you will have to do aggregations: minimum, maximum, average, mean, sum, etc. Databases already have all this and with combination of other features they can save you a ton of time you can otherwise spend on writing oh so many scripts and abstractions over files. Not to mention just how cumbersome the file management becomes.
Databases are extensible and supported by many languages. You save a datetime in database with Python, you read datetime with PHP. No hassles with how you are going to have to encode your data.
Databases support indexes for faster lookup.
My personal favourite is PostgreSQL, which has a number of nice features. It supports BRIN index, a lightweight index, perfect for huge datasets with naturally ordered fields, such as timestamps. If you're low on disk, you can extend it with cstore_fdw, a columnar oriented datastore, which supports compression. And if you still want to use flat files, you can write a foreign data wrapper (also possible with Python) and still use SQL to access the data.

Unless you're consuming the files in the same language, avoid language specific formats and structures. Always.
If you're going between 2 or more languages, use a common, plain text data format like JSON or XML that can be easily (often natively) parsed by most languages and tools.
If you follow this advice and your storing plain text, then use compression on the stored file--that's how you conserve space. Typical well-structured JSON tends to compresses really well (assuming simple text content).
Once again, choose a compression format like gzip that's widely supported in by languages or their core libraries. PHP for example has a native function gzopen() and python has lib\gzip.py in the standard python library.

I doubt it is possible without extremely efficient compression.
6TB / 10 year/ 365 days / 24 hrs / 250 files = 270 KB per file.
In ideal case. In real word size of cluster matters.
If you have 36,000 “small arrays” to fit into each file, you have only 7 bytes per array, which is not enough to store even proper datetime object alone.

One idea that comes to my mind if you want to save space. You better store only values and discard timestamps. Produce files with only data and make sure that you created a kind of index (formula) that given a timestamp (year/month/day/hour/min/sec...) results in the position of the data inside of the file (and of course the file that you have to go for). Even, if you check twice you will discover that if you use an "smart" naming scheme for the files you can avoid to store information about year/month/day/hour, since part of the index could be the file name. That all depends on how do you implement your "index" system, but pushing to an extreme version you could forget about timestamps and focus only in data.
Regarding data format, as aforementioned, I would definitively go on language independent format as XML, JSON... Who know which languages and possibilities will you have in ten years ;)

Related

yaml_parse works also with JSON ? what other formats I could use? [duplicate]

What are the differences between YAML and JSON, specifically considering the following things?
Performance (encode/decode time)
Memory consumption
Expression clarity
Library availability, ease of use (I prefer C)
I was planning to use one of these two in our embedded system to store configure files.
Related:
Should I use YAML or JSON to store my Perl data?
Technically YAML is a superset of JSON. This means that, in theory at least, a YAML parser can understand JSON, but not necessarily the other way around.
See the official specs, in the section entitled "YAML: Relation to JSON".
In general, there are certain things I like about YAML that are not available in JSON.
As #jdupont pointed out, YAML is visually easier to look at. In fact the YAML homepage is itself valid YAML, yet it is easy for a human to read.
YAML has the ability to reference other items within a YAML file using "anchors." Thus it can handle relational information as one might find in a MySQL database.
YAML is more robust about embedding other serialization formats such as JSON or XML within a YAML file.
In practice neither of these last two points will likely matter for things that you or I do, but in the long term, I think YAML will be a more robust and viable data serialization format.
Right now, AJAX and other web technologies tend to use JSON. YAML is currently being used more for offline data processes. For example, it is included by default in the C-based OpenCV computer vision package, whereas JSON is not.
You will find C libraries for both JSON and YAML. YAML's libraries tend to be newer, but I have had no trouble with them in the past. See for example Yaml-cpp.
Differences:
YAML, depending on how you use it, can be more readable than JSON
JSON is often faster and is probably still interoperable with more systems
It's possible to write a "good enough" JSON parser very quickly
Duplicate keys, which are potentially valid JSON, are definitely invalid YAML.
YAML has a ton of features, including comments and relational anchors. YAML syntax is accordingly quite complex, and can be hard to understand.
It is possible to write recursive structures in yaml: {a: &b [*b]}, which will loop infinitely in some converters. Even with circular detection, a "yaml bomb" is still possible (see xml bomb).
Because there are no references, it is impossible to serialize complex structures with object references in JSON. YAML serialization can therefore be more efficient.
In some coding environments, the use of YAML can allow an attacker to execute arbitrary code.
Observations:
Python programmers are generally big fans of YAML, because of the use of indentation, rather than bracketed syntax, to indicate levels.
Many programmers consider the attachment of "meaning" to indentation a poor choice.
If the data format will be leaving an application's environment, parsed within a UI, or sent in a messaging layer, JSON might be a better choice.
YAML can be used, directly, for complex tasks like grammar definitions, and is often a better choice than inventing a new language.
Bypassing esoteric theory
This answers the title, not the details as most just read the title from a search result on google like me so I felt it was necessary to explain from a web developer perspective.
YAML uses space indentation, which is familiar territory for Python developers.
JavaScript developers love JSON because it is a subset of JavaScript and can be directly interpreted and written inside JavaScript, along with using a shorthand way to declare JSON, requiring no double quotes in keys when using typical variable names without spaces.
There are a plethora of parsers that work very well in all languages for both YAML and JSON.
YAML's space format can be much easier to look at in many cases because the formatting requires a more human-readable approach.
YAML's form while being more compact and easier to look at can be deceptively difficult to hand edit if you don't have space formatting visible in your editor. Tabs are not spaces so that further confuses if you don't have an editor to interpret your keystrokes into spaces.
JSON is much faster to serialize and deserialize because of significantly less features than YAML to check for, which enables smaller and lighter code to process JSON.
A common misconception is that YAML needs less punctuation and is more compact than JSON but this is completely false. Whitespace is invisible so it seems like there are less characters, but if you count the actual whitespace which is necessary to be there for YAML to be interpreted properly along with proper indentation, you will find YAML actually requires more characters than JSON. JSON doesn't use whitespace to represent hierarchy or grouping and can be easily flattened with unnecessary whitespace removed for more compact transport.
The Elephant in the room: The Internet itself
JavaScript so clearly dominates the web by a huge margin and JavaScript developers prefer using JSON as the data format overwhelmingly along with popular web APIs so it becomes difficult to argue using YAML over JSON when doing web programming in the general sense as you will likely be outvoted in a team environment. In fact, the majority of web programmers aren't even aware YAML exists, let alone consider using it.
If you are doing any web programming, JSON is the default way to go because no translation step is needed when working with JavaScript so then you must come up with a better argument to use YAML over JSON in that case.
This question is 6 years old, but strangely, none of the answers really addresses all four points (speed, memory, expressiveness, portability).
Speed
Obviously this is implementation-dependent, but because JSON is so widely used, and so easy to implement, it has tended to receive greater native support, and hence speed. Considering that YAML does everything that JSON does, plus a truckload more, it's likely that of any comparable implementations of both, the JSON one will be quicker.
However, given that a YAML file can be slightly smaller than its JSON counterpart (due to fewer " and , characters), it's possible that a highly optimised YAML parser might be quicker in exceptional circumstances.
Memory
Basically the same argument applies. It's hard to see why a YAML parser would ever be more memory efficient than a JSON parser, if they're representing the same data structure.
Expressiveness
As noted by others, Python programmers tend towards preferring YAML, JavaScript programmers towards JSON. I'll make these observations:
It's easy to memorise the entire syntax of JSON, and hence be very confident about understanding the meaning of any JSON file. YAML is not truly understandable by any human. The number of subtleties and edge cases is extreme.
Because few parsers implement the entire spec, it's even harder to be certain about the meaning of a given expression in a given context.
The lack of comments in JSON is, in practice, a real pain.
Portability
It's hard to imagine a modern language without a JSON library. It's also hard to imagine a JSON parser implementing anything less than the full spec. YAML has widespread support, but is less ubiquitous than JSON, and each parser implements a different subset. Hence YAML files are less interoperable than you might think.
Summary
JSON is the winner for performance (if relevant) and interoperability. YAML is better for human-maintained files. HJSON is a decent compromise although with much reduced portability. JSON5 is a more reasonable compromise, with well-defined syntax.
GIT and YAML
The other answers are good. Read those first. But I'll add one other reason to use YAML sometimes: git.
Increasingly, many programming projects use git repositories for distribution and archival. And, while a git repo's history can equally store JSON and YAML files, the "diff" method used for tracking and displaying changes to a file is line-oriented. Since YAML is forced to be line-oriented, any small changes in a YAML file are easier to see by a human.
It is true, of course, that JSON files can be "made pretty" by sorting the strings/keys and adding indentation. But this is not the default and I'm lazy.
Personally, I generally use JSON for system-to-system interaction. I often use YAML for config files, static files, and tracked files. (I also generally avoid adding YAML relational anchors. Life is too short to hunt down loops.)
Also, if speed and space are really a concern, I don't use either. You might want to look at BSON.
I find YAML to be easier on the eyes: less parenthesis, "" etc. Although there is the annoyance of tabs in YAML... but one gets the hang of it.
In terms of performance/resources, I wouldn't expect big differences between the two.
Futhermore, we are talking about configuration files and so I wouldn't expect a high frequency of encode/decode activity, no?
Technically YAML offers a lot more than JSON (YAML v1.2 is a superset of JSON):
comments
anchors and inheritance - example of 3 identical items:
item1: &anchor_name
name: Test
title: Test title
item2: *anchor_name
item3:
<<: *anchor_name
# You may add extra stuff.
...
Most of the time people will not use those extra features and the main difference is that YAML uses indentation whilst JSON uses brackets. This makes YAML more concise and readable (for the trained eye).
Which one to choose?
YAML extra features and concise notation makes it a good choice for configuration files (non-user provided files).
JSON limited features, wide support, and faster parsing makes it a great choice for interoperability and user provided data.
If you don't need any features which YAML has and JSON doesn't, I would prefer JSON because it is very simple and is widely supported (has a lot of libraries in many languages). YAML is more complex and has less support. I don't think the parsing speed or memory use will be very much different, and maybe not a big part of your program's performance.
Benchmark results
Below are the results of a benchmark to compare YAML vs JSON loading times, on Python and Perl
JSON is much faster, at the expense of some readability, and features such as comments
Test method
100 sequential runs on a fast machine, average number of seconds
The dataset was a 3.44MB JSON file, containing movie data scraped from Wikipedia
https://raw.githubusercontent.com/prust/wikipedia-movie-data/master/movies.json
Linked to from: https://github.com/jdorfman/awesome-json-datasets
Results
Python 3.8.3 timeit
JSON: 0.108
YAML CLoader: 3.684
YAML: 29.763
Perl 5.26.2 Benchmark::cmpthese
JSON XS: 0.107
YAML XS: 0.574
YAML Syck: 1.050
Perl 5.26.2 Dumbbench (Brian D Foy, excludes outliers)
JSON XS: 0.102
YAML XS: 0.514
YAML Syck: 1.027
From: Arnaud Lauret Book “The Design of Web APIs.” :
The JSON data format
JSON is a text data format based on how the JavaScript programming language describes data but is, despite its name, completely language-independent (see https://www.json.org/). Using JSON, you can describe objects containing unordered name/value pairs and also arrays or lists containing ordered values, as shown in this figure.
An object is delimited by curly braces ({}). A name is a quoted string ("name") and is sep- arated from its value by a colon (:). A value can be a string like "value", a number like 1.23, a Boolean (true or false), the null value null, an object, or an array. An array is delimited by brackets ([]), and its values are separated by commas (,).
The JSON format is easily parsed using any programming language. It is also relatively easy to read and write. It is widely adopted for many uses such as databases, configura- tion files, and, of course, APIs.
YAML
YAML (YAML Ain’t Markup Language) is a human-friendly, data serialization format. Like JSON, YAML (http://yaml.org) is a key/value data format. The figure shows a comparison of the two.
Note the following points:
There are no double quotes (" ") around property names and values in YAML.
JSON’s structural curly braces ({}) and commas (,) are replaced by newlines and
indentation in YAML.
Array brackets ([]) and commas (,) are replaced by dashes (-) and newlines in
YAML.
Unlike JSON, YAML allows comments beginning with a hash mark (#).
It is relatively easy to convert one of those formats into the other. Be forewarned though, you will lose comments when converting a YAML document to JSON.
Since this question now features prominently when searching for YAML and JSON, it's worth noting one rarely-cited difference between the two: license. JSON purports to have a license which JSON users must adhere to (including the legally-ambiguous "shall be used for Good, not Evil"). YAML carries no such license claim, and that might be an important difference (to your lawyer, if not to you).
Sometimes you don't have to decide for one over the other.
In Go, for example, you can have both at the same time:
type Person struct {
Name string `json:"name" yaml:"name"`
Age int `json:"age" yaml:"age"`
}
I find both YAML and JSON to be very effective. The only two things that really dictate when one is used over the other for me is one, what the language is used most popularly with. For example, if I'm using Java, Javascript, I'll use JSON. For Java, I'll use their own objects, which are pretty much JSON but lacking in some features, and convert it to JSON if I need to or make it in JSON in the first place. I do that because that's a common thing in Java and makes it easier for other Java developers to modify my code. The second thing is whether I'm using it for the program to remember attributes, or if the program is receiving instructions in the form of a config file, in this case I'll use YAML, because it's very easily human read, has nice looking syntax, and is very easy to modify, even if you have no idea how YAML works. Then, the program will read it and convert it to JSON, or whatever is preferred for that language.
In the end, it honestly doesn't matter. Both JSON and YAML are easily read by any experienced programmer.
If you are concerned about better parsing speed then storing the data in JSON is the option. I had to parse the data from a location where the file was subject to modification from other users and hence I used YAML as it provides better readability compared to JSON.
And you can also add comments in the YAML file which can't be done in a JSON file.
JSON encodes six data types: Objects (mappings), Arrays, Strings Numbers, Booleans and Null. It is extremely easy for a machine to parse and provides very little flexibility. The specification is about a page and a half.
YAML allows the encoding of arbitrary Python data and other crazy crap (which leads to vulnerabilities when decoding it). It is hard to parse because it offers so much flexibility. The specification for YAML was 86 pages, the last time I checked. YAML syntax is obviously influenced by Python, but maybe they should have been a little more influenced by the Python philosophy on a few points: e.g. “there should be one—and preferably only one—obvious way to do it” and “simple is better than complex.”
The main benefit of YAML over JSON is that it’s easier for humans to read and edit, which makes it a natural choice for configuration files.
These days, I’m leaning towards TOML for configuration files. It’s not as pretty or as flexible as YAML, but it’s easier both for machines and humans to parse. The syntax is (almost) a superset of INI syntax, but it parses out to JSON-like data structures, adding only one additional type: the date type.

How Transport a C structure in a Embedded Device to PHP

I have a fairly big (Around 50bytes) structure in a Embedded device to be transported to a PHP script which is to be stored in a DB. Most of the elements in the structure are bits and some are integers and chars.
What I plan is use a union in C (Embedded Device) to take the data as a binary array and do a base64 encoding and upload through a URL string as a variable.
Now In php I have a big array which needs to be sepertaed as flags and integers to be store in DB.
This is my task. which will be the suitable method to do the work.
Thanks
If you send as binary, then you will have to decide on and enforce byte order at each end regardless of platform byte order. Enforce network byte order by using the POSIX byte order conversion functions.
Also you cannot rely on compiler generated structure bit-fields to pack and unpack identically on both platforms so for the bit fields you must use shifting-and-masking to pack and unpack and to be careful of bitfields that span byte or word boundaries.
A more easily portable solution perhaps is to transfer using a structured text format such as XML or JSON, or a proprietary format of your own design if these are too verbose.
Structures created in C you can decode in php using unpack() function.

Compacting string data for storage and retrieval

I have some text data I would like to store in a mysql database. I currently have the data stored in a variable as a string.
I'm concerned that the table will become quite large due to the amount of text data I have for each row.
Therefore, what is the most easiest way (preferably php built in functions) of compacting this string data in a format ideal for storage and retrieval?
You could GZIP the string with GZEncode.
That's pretty standard and thus should be reversible from other languages if you want to.
I would advise storing a Base64 version of the result.
If you're using InnoDB you can enable compression on entire tables which doesn't impact your code at all.
ALTER TABLE database.tableName ENGINE='InnoDB' ROW_FORMAT=COMPRESSED KEY_BLOCK_SIZE=8;
You can alter the KEY_BLOCK_SIZE to smaller values to get more compression (depending on the data), but this adds more overhead to the CPU.
After testing a range of tables, I found a KEY_BLOCK_SIZE of 8 to be a good balance of compression vs performance.

rawurlencode for storing data

I have always used rawurlencode to store user entered data into my mysql databases. The main reason I do this is so that stroing foreign characters is very simple I find. I'd then use rawurldecode to retrieve and display the data.
I read somewhere that rawurlencode was not meant for this purpose. Are there any disadvantages to what I'm doing?
So let's say I have a German address with many characters like umlauts etc. What is the simplest way to store this in a mysql database with no risks of it coming out wrong and being searchable using a search script? So far rawurelencode has been excellent for our system. Perhaps the practise can be improved upon by only encoding foreign letters and not common characters like spaces etc, which is a waste of space I totally agree.
Sure there are.
Let's start with the practical: for a large class of characters you are spending 3 bytes of storage for every byte of data. The description of rawurlencode (and of course the RFC) say that those characters are
all non-alphanumeric characters except -_.~
This means that there is a total of 26 + 26 + 10 (alphanumeric) + 4 (special exceptions) = 66 characters for which you do not waste space.
Then there are also the logical drawbacks: You are not storing the data itself, but rather a representation of the data tailored to URLs. Unless the data itself is URLs, that's not what you should be doing.
Drawbacks I can think of:
Waste of disk space.
Waste of CPU cycles encoding and decoding on every read and every write.
Additional complexity (you can't even inspect data with a MySQL client).
Impossibility to use full text searches.
URL encoding is not necessarily unique (there're at least two RFCs). It may not lead to data loss but it can lead to duplicate data (e.g., unique indexes where two rows actually contain the same piece of data).
You can accidentally encode a non-string piece of data such as a date: 2012-04-20%2013%3A23%3A00
But the main consideration is that such technique is completely arbitrary and unnecessary since MySQL doesn't have the least problem storing the complete Unicode catalogue. You could also decide to swap e's and o's in all strings: Holle, werdl!. Your app would run fine but it would not provide any added value.
Update: As Your Common Sense points out, a SQL clause as basic as ORDER BYis no longer usable. It's not that international chars will be ignored; you'll basically get an arbitrary sort order based on the ASCII code of the % and hexadecimal characters. If you can't SELECT * FROM city ORDER BY city_name reliably, you've rendered your DB useless.
I am using a fork to eat a soup
I am using money bills to fire the coals for BBQ
I am using a kettle to boil eggs.
I am using a microscope to hammer the nails.
Are there any disadvantages to what I'm doing?
YES
You are using a tool not on purpose. This is always a disadvantage.
A sane human being alway using a tool that is intended for the certain job. Not some randomly picked one. Especially if there is no shortage in the right tool supply.
URL encoding is not intended to be used with database, as one can tell from the name. That's alone reason enough for the sane developer. Take a look around: find the proper tool.
There is a thing called "common sense" - a thing widely used in the regular life but for some reason always absent in the php world.
A common sense can warn us: if we're using a wrong tool, it may spoil the work. Sooner or later it will spoil it. No need to ask for the certain details - it's a general rule. We are learning this rule at about age of 5.
Why not to use it while playing with some web thingies too?
Why not to ask yourself a question:
What's wrong with storing foreign characters at all?
urlencode makes stroing foreign characters very simple
Any hardships you encountered without urlencode?
Although I feel that common sense should be enough to answer the question, people always look for the "omen", the proof. Here you are:
Database's job is not limited to just storing and retrieving data. A plain text file can handle such a primitive task as well.
Data manipulations is what we are using databases for.
Most widely used ones are sorting and filtering.
Such a quite intelligent thing as a database can sort and filter data character-insensitive, which is very handy feature. But of course it can be done only if characters being saved as is, not as some random codes.
Sorting texts also may use ordering other than just binary order in the character table. Some umlaut characters may be present at the other parts of the table but database collation will put them in the right place. Of course it can be done only if characters being saved as is, not as some random codes.
Sometimes we have to manipulate the data that already stored in the database. Say, cut some piece from the string and compare with the entered value. How it is supposed to be done with urlencoded data?

Large amounts of text - mysql or flatfile?

I am writing a web application in PHP that will store large numbers of blocks of arbitrary length text. Is MySQL well suited for this task with a longtext field or similar, or should I store each block of text in its own file and use a MySQL table for indexes and filenames? Think online bulletin board type stuff, like how you would store each users posts.
Yes, MySQL is the way to go. A flat file would take much longer to search etc.
Mysql all the way. Much more efficient.

Categories