I am trying to find an execution module that will allow me to pass a file through the Jinja templating engine and supply the arguments that should be replaced. There exists a file.managed state module that accomplishes this behavior:
my cool state:
file.managed:
- source: salt://my/cool/file.xml
- name: 'C:\Program Files\My dir\file.xml'
- template: jinja
- context:
someVar: 'some value'
another_var: 12345
However, I cannot find an execution module that can do this. There is file.manage_file but it has a bunch of required arguments that I don't care about: sfn, ret, source, source_sum, user, group, mode, attrs, saltenv, backup -- file.manage_file will fail if you do not provide values for these.
The closest module function I've found is cp.get_template but it doesn't allow you to pass in context or defaults, so I've had to templatize my XML file to read in data from the pillar like so:
{%- from "map.jinja" import my_vars with context %}
<?xml version="1.0" encoding="UTF-8"?>
<root>
<some-element>{{ my_vars.some-element }}</some-element>
<another-thing>{{ my_vars.another-thing }}</another-thing>
</root>
This works, but then I can only render my XML with pillar data -- I want to be able to pass in variables when calling my execution module and use those variables to render my XML. Is there any way to accomplish this behavior with an execution module?
I figured this out using a combination of execution modules:
# Get template file
templateContent = __salt__['cp.get_file_str'](
'salt://my/cool/file.xml'
)
# Render file with Jinja
renderedContent = __salt__['file.apply_template_on_contents'](
contents = templateContent,
template = 'jinja',
saltenv = 'base',
defaults = defaults,
context = context
)
# Write the rendered file to disk
__salt__['file.write'](
'/opt/some/path/on/minion/file.xml',
renderedContent
)
Two things to consider:
file.apply_template_on_contents requires that you supply both the defaults and context args. This actually worked out pretty well for my use-case because I set defaults using values from a pillar that is applied to all minions, and context is the user-supplied overrides.
cp.get_file_str returns the file contents as a string -- I don't know how this would perform on a very large file, but for smaller configuration files I don't see an issue.
A relative sqlalchemy path to a sqlite database can be written as:
sqlite:///folder/db_file.db
And an absolute one as:
sqlite:////home/user/folder/db_file.db
Is it possible to write a path relative to home? Like this:
sqlite:///~/folder/db_file.db
Or even better, can the path contain environment variables?
sqlite:////${MY_FOLDER}/db_file.db
This is the context of an alembic.ini file. So if the previous objectives are not possible directly, may I be able to cheat using variable substitution?
[alembic]
script_location = db_versions
sqlalchemy.url = sqlite:///%(MY_FOLDER)s.db
...
I have gone around this issue by modifying the values in the config object just after env.py imports it:
# this is the Alembic Config object, which provides
# access to the values within the .ini file in use.
config = context.config
# import my custom configuration
from my_app import MY_DB_URI
# overwrite the desired value
config.set_main_option("sqlalchemy.url", MY_DB_URI)
Now config.get_main_option("sqlalchemy.url") returns the MY_DB_URI you wanted.
As others have pointed out, one key is 3 slashes for relative, 4 for absolute.
But it took for me than just that...
Had trouble with just a string, I had to do this:
db_dir = "../../database/db.sqlite"
print(f'os.path.abspath(db_dir): {str(os.path.abspath(db_dir))}')
SQLALCHEMY_DATABASE_URI = "sqlite:///" + os.path.abspath(db_dir) # works
# SQLALCHEMY_DATABASE_URI = "sqlite:///" + db_dir # fails
From the alembic documentation (emphasis mine):
sqlalchemy.url - A URL to connect to the database via SQLAlchemy. This configuration value is only used if the env.py file calls upon them; in the “generic” template, the call to config.get_main_option("sqlalchemy.url") in the run_migrations_offline() function and the call to engine_from_config(prefix="sqlalchemy.") in the run_migrations_online() function are where this key is referenced. If the SQLAlchemy URL should come from some other source, such as from environment variables or a global registry, or if the migration environment makes use of multiple database URLs, the developer is encouraged to alter the env.py file to use whatever methods are appropriate in order to acquire the database URL or URLs.
So for this case, the sqlalchemy url format can be circunvented and generated by python itself.
What are the strategies to embed a unique version number in a Spring application?
I've got an app using Spring Boot and Spring Web.
Its matured enough that I want to version it and see it displayed on screen at run time.
I believe what you are looking for is generating this version number during build time (Usually by build tools like Ant, Maven or Gradle) as part of their build task chain.
I believe a quite common approach is to either put the version number into the Manifest.mf of the produced JAR and then read it, or create a file that is part of the produced JAR that can be read by your application.
Another solution would be just using Spring Boot's banner customization options described here: http://docs.spring.io/spring-boot/docs/current/reference/html/boot-features-spring-application.html#boot-features-banner
However, this will only allow you to change spring-boot banner.
I also believe that Spring Boot exposes product version that is set in Manifest.MF of your application. To achieve this you will need to make sure Implementation-Version attribute of the manifest is set.
Custom solution for access anywhere in the code
Lets assume you would like to have a version.properties file in your src/main/resources that contains your version information. It will contain placeholders instead of actual values so that these placeholders can be expanded during build time.
version=${prodVersion}
build=${prodBuild}
timestamp=${buildTimestamp}
Now that you have a file like this you need to fill it with actual data. I use Gradle so there I would make sure that processResources task which is automatically running for builds is expanding resources. Something like this should do the trick in the build.gradle file for Git-based code:
import org.codehaus.groovy.runtime.*
import org.eclipse.jgit.api.*
def getGitBranchCommit() {
try {
def git = Git.open(project.file(project.getRootProject().getProjectDir()));
def repo = git.getRepository();
def id = repo.resolve(repo.getFullBranch());
return id.abbreviate(7).name()
} catch (IOException ex) {
return "UNKNOWN"
}
}
processResources {
filesMatching("**/version.properties") {
expand (
"prodVersion": version,
"prodBuild": getGitBranchCommit(),
"buildTimestamp": DateGroovyMethods.format(new Date(), 'yyyy-MM-dd HH:mm')
)
}
}
processResources.outputs.upToDateWhen{ false }
In the code about the following is happening:
We defined a function that can take a build number out of the VCS
(in this case Git). The commit hash is limited to 7 characters.
We configure the processResources task to process
version.properties file and fill it with our variables.
prodVersion is taken from Gradle project version. It's usually set
as version in gradle.properties file (part of the general build
setup).
As a last step we ensure that it's always updated (Gradle
has some mechanics to detect if files ened to be processed
Considering you are on SVN, you will need to have a getSvnBranchCommit() method instead. You could for instance use SVNKit or similar for this.
The last thing that is missing now is reading of the file for use in your application.
This could be achieved by simply reading a classpath resource and parsing it into java.util.Properties. You could take it one step further and for instance create accessor methods specifically for each field, e.g getVersion(), getBuild(), etc.
Hope this helps a bit (even though may not be 100% applicable straight off)
Maven can be used to track the version number, e.g.:
<!-- pom.xml -->
<version>2.0.3</version>
Spring Boot can refer to the version, and expose it via REST using Actuator:
# application.properties
endpoints.info.enabled=true
info.app.version=#project.version#
Then use Ajax to render the version in the browser, for example using Polymer iron-ajax:
<!-- about-page.html -->
<iron-ajax auto url="/info" last-response="{{info}}"></iron-ajax>
Application version is: [[info.app.version]]
This will then show in the browser as:
Application version is: 2.0.3
I'm sure you've probably figured something out since this is an older question, but here's what I just did and it looks good. (Getting it into the banner requires you to duplicate a lot).
I'd recommend switching to git (it's a great SVN client too), and then using this in your build.gradle:
// https://github.com/n0mer/gradle-git-properties
plugins {
id "com.gorylenko.gradle-git-properties" version "1.4.17"
}
// http://docs.spring.io/spring-boot/docs/current/reference/html/deployment-install.html
springBoot {
buildInfo() // create META-INF/build-info.properties
}
bootRun.dependsOn = [assemble]
And this in your SpringBoot application:
#Resource
GitProperties props;
#Resource
BuildProperties props2;
Or this way to expose those properties into the standard spring environment:
#SpringBootApplication
#PropertySources({
#PropertySource("classpath:git.properties"),
#PropertySource("classpath:META-INF/build-info.properties")
})
public class MySpringBootApplication {
and then referencing the individual properties as needed.
#Value("${git.branch}")
String gitBranch;
#Value("${build.time}")
String buildTime;
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In my endless quest in over-complicating simple stuff, I am researching the most 'Pythonic' way to provide global configuration variables inside the typical 'config.py' found in Python egg packages.
The traditional way (aah, good ol' #define!) is as follows:
MYSQL_PORT = 3306
MYSQL_DATABASE = 'mydb'
MYSQL_DATABASE_TABLES = ['tb_users', 'tb_groups']
Therefore global variables are imported in one of the following ways:
from config import *
dbname = MYSQL_DATABASE
for table in MYSQL_DATABASE_TABLES:
print table
or:
import config
dbname = config.MYSQL_DATABASE
assert(isinstance(config.MYSQL_PORT, int))
It makes sense, but sometimes can be a little messy, especially when you're trying to remember the names of certain variables. Besides, providing a 'configuration' object, with variables as attributes, might be more flexible. So, taking a lead from bpython config.py file, I came up with:
class Struct(object):
def __init__(self, *args):
self.__header__ = str(args[0]) if args else None
def __repr__(self):
if self.__header__ is None:
return super(Struct, self).__repr__()
return self.__header__
def next(self):
""" Fake iteration functionality.
"""
raise StopIteration
def __iter__(self):
""" Fake iteration functionality.
We skip magic attribues and Structs, and return the rest.
"""
ks = self.__dict__.keys()
for k in ks:
if not k.startswith('__') and not isinstance(k, Struct):
yield getattr(self, k)
def __len__(self):
""" Don't count magic attributes or Structs.
"""
ks = self.__dict__.keys()
return len([k for k in ks if not k.startswith('__')\
and not isinstance(k, Struct)])
and a 'config.py' that imports the class and reads as follows:
from _config import Struct as Section
mysql = Section("MySQL specific configuration")
mysql.user = 'root'
mysql.pass = 'secret'
mysql.host = 'localhost'
mysql.port = 3306
mysql.database = 'mydb'
mysql.tables = Section("Tables for 'mydb'")
mysql.tables.users = 'tb_users'
mysql.tables.groups = 'tb_groups'
and is used in this way:
from sqlalchemy import MetaData, Table
import config as CONFIG
assert(isinstance(CONFIG.mysql.port, int))
mdata = MetaData(
"mysql://%s:%s#%s:%d/%s" % (
CONFIG.mysql.user,
CONFIG.mysql.pass,
CONFIG.mysql.host,
CONFIG.mysql.port,
CONFIG.mysql.database,
)
)
tables = []
for name in CONFIG.mysql.tables:
tables.append(Table(name, mdata, autoload=True))
Which seems a more readable, expressive and flexible way of storing and fetching global variables inside a package.
Lamest idea ever? What is the best practice for coping with these situations? What is your way of storing and fetching global names and variables inside your package?
How about just using the built-in types like this:
config = {
"mysql": {
"user": "root",
"pass": "secret",
"tables": {
"users": "tb_users"
}
# etc
}
}
You'd access the values as follows:
config["mysql"]["tables"]["users"]
If you are willing to sacrifice the potential to compute expressions inside your config tree, you could use YAML and end up with a more readable config file like this:
mysql:
- user: root
- pass: secret
- tables:
- users: tb_users
and use a library like PyYAML to conventiently parse and access the config file
I like this solution for small applications:
class App:
__conf = {
"username": "",
"password": "",
"MYSQL_PORT": 3306,
"MYSQL_DATABASE": 'mydb',
"MYSQL_DATABASE_TABLES": ['tb_users', 'tb_groups']
}
__setters = ["username", "password"]
#staticmethod
def config(name):
return App.__conf[name]
#staticmethod
def set(name, value):
if name in App.__setters:
App.__conf[name] = value
else:
raise NameError("Name not accepted in set() method")
And then usage is:
if __name__ == "__main__":
# from config import App
App.config("MYSQL_PORT") # return 3306
App.set("username", "hi") # set new username value
App.config("username") # return "hi"
App.set("MYSQL_PORT", "abc") # this raises NameError
.. you should like it because:
uses class variables (no object to pass around/ no singleton required),
uses encapsulated built-in types and looks like (is) a method call on App,
has control over individual config immutability, mutable globals are the worst kind of globals.
promotes conventional and well named access / readability in your source code
is a simple class but enforces structured access, an alternative is to use #property, but that requires more variable handling code per item and is object-based.
requires minimal changes to add new config items and set its mutability.
--Edit--:
For large applications, storing values in a YAML (i.e. properties) file and reading that in as immutable data is a better approach (i.e. blubb/ohaal's answer).
For small applications, this solution above is simpler.
How about using classes?
# config.py
class MYSQL:
PORT = 3306
DATABASE = 'mydb'
DATABASE_TABLES = ['tb_users', 'tb_groups']
# main.py
from config import MYSQL
print(MYSQL.PORT) # 3306
Let's be honest, we should probably consider using a Python Software Foundation maintained library:
https://docs.python.org/3/library/configparser.html
Config example: (ini format, but JSON available)
[DEFAULT]
ServerAliveInterval = 45
Compression = yes
CompressionLevel = 9
ForwardX11 = yes
[bitbucket.org]
User = hg
[topsecret.server.com]
Port = 50022
ForwardX11 = no
Code example:
>>> import configparser
>>> config = configparser.ConfigParser()
>>> config.read('example.ini')
>>> config['DEFAULT']['Compression']
'yes'
>>> config['DEFAULT'].getboolean('MyCompression', fallback=True) # get_or_else
Making it globally-accessible:
import configpaser
class App:
__conf = None
#staticmethod
def config():
if App.__conf is None: # Read only once, lazy.
App.__conf = configparser.ConfigParser()
App.__conf.read('example.ini')
return App.__conf
if __name__ == '__main__':
App.config()['DEFAULT']['MYSQL_PORT']
# or, better:
App.config().get(section='DEFAULT', option='MYSQL_PORT', fallback=3306)
....
Downsides:
Uncontrolled global mutable state.
A small variation on Husky's idea that I use. Make a file called 'globals' (or whatever you like) and then define multiple classes in it, as such:
#globals.py
class dbinfo : # for database globals
username = 'abcd'
password = 'xyz'
class runtime :
debug = False
output = 'stdio'
Then, if you have two code files c1.py and c2.py, both can have at the top
import globals as gl
Now all code can access and set values, as such:
gl.runtime.debug = False
print(gl.dbinfo.username)
People forget classes exist, even if no object is ever instantiated that is a member of that class. And variables in a class that aren't preceded by 'self.' are shared across all instances of the class, even if there are none. Once 'debug' is changed by any code, all other code sees the change.
By importing it as gl, you can have multiple such files and variables that lets you access and set values across code files, functions, etc., but with no danger of namespace collision.
This lacks some of the clever error checking of other approaches, but is simple and easy to follow.
Similar to blubb's answer. I suggest building them with lambda functions to reduce code. Like this:
User = lambda passwd, hair, name: {'password':passwd, 'hair':hair, 'name':name}
#Col Username Password Hair Color Real Name
config = {'st3v3' : User('password', 'blonde', 'Steve Booker'),
'blubb' : User('12345678', 'black', 'Bubb Ohaal'),
'suprM' : User('kryptonite', 'black', 'Clark Kent'),
#...
}
#...
config['st3v3']['password'] #> password
config['blubb']['hair'] #> black
This does smell like you may want to make a class, though.
Or, as MarkM noted, you could use namedtuple
from collections import namedtuple
#...
User = namedtuple('User', ['password', 'hair', 'name']}
#Col Username Password Hair Color Real Name
config = {'st3v3' : User('password', 'blonde', 'Steve Booker'),
'blubb' : User('12345678', 'black', 'Bubb Ohaal'),
'suprM' : User('kryptonite', 'black', 'Clark Kent'),
#...
}
#...
config['st3v3'].password #> passwd
config['blubb'].hair #> black
I did that once. Ultimately I found my simplified basicconfig.py adequate for my needs. You can pass in a namespace with other objects for it to reference if you need to. You can also pass in additional defaults from your code. It also maps attribute and mapping style syntax to the same configuration object.
please check out the IPython configuration system, implemented via traitlets for the type enforcement you are doing manually.
Cut and pasted here to comply with SO guidelines for not just dropping links as the content of links changes over time.
traitlets documentation
Here are the main requirements we wanted our configuration system to have:
Support for hierarchical configuration information.
Full integration with command line option parsers. Often, you want to read a configuration file, but then override some of the values with command line options. Our configuration system automates this process and allows each command line option to be linked to a particular attribute in the configuration hierarchy that it will override.
Configuration files that are themselves valid Python code. This accomplishes many things. First, it becomes possible to put logic in your configuration files that sets attributes based on your operating system, network setup, Python version, etc. Second, Python has a super simple syntax for accessing hierarchical data structures, namely regular attribute access (Foo.Bar.Bam.name). Third, using Python makes it easy for users to import configuration attributes from one configuration file to another.
Fourth, even though Python is dynamically typed, it does have types that can be checked at runtime. Thus, a 1 in a config file is the integer ‘1’, while a '1' is a string.
A fully automated method for getting the configuration information to the classes that need it at runtime. Writing code that walks a configuration hierarchy to extract a particular attribute is painful. When you have complex configuration information with hundreds of attributes, this makes you want to cry.
Type checking and validation that doesn’t require the entire configuration hierarchy to be specified statically before runtime. Python is a very dynamic language and you don’t always know everything that needs to be configured when a program starts.
To acheive this they basically define 3 object classes and their relations to each other:
1) Configuration - basically a ChainMap / basic dict with some enhancements for merging.
2) Configurable - base class to subclass all things you'd wish to configure.
3) Application - object that is instantiated to perform a specific application function, or your main application for single purpose software.
In their words:
Application: Application
An application is a process that does a specific job. The most obvious application is the ipython command line program. Each application reads one or more configuration files and a single set of command line options and then produces a master configuration object for the application. This configuration object is then passed to the configurable objects that the application creates. These configurable objects implement the actual logic of the application and know how to configure themselves given the configuration object.
Applications always have a log attribute that is a configured Logger. This allows centralized logging configuration per-application.
Configurable: Configurable
A configurable is a regular Python class that serves as a base class for all main classes in an application. The Configurable base class is lightweight and only does one things.
This Configurable is a subclass of HasTraits that knows how to configure itself. Class level traits with the metadata config=True become values that can be configured from the command line and configuration files.
Developers create Configurable subclasses that implement all of the logic in the application. Each of these subclasses has its own configuration information that controls how instances are created.