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.
I'm trying to find machines that publish a certain type of message. Therefore I'm inspecting the list of rosnodes rosnode.rosnode_listnodes() and would like to get the IP address of the corresponding machines running a specific node.
There is a method rosnode.lookup_uri(...), but I have no idea how to use it. I also looked into the rospy, rosgraph, names and network modules - so far without success. Among other information,rosnode.rosnode_info(node_name) prints the URI. But it might be a rather expensive method and, unfortunately, it returns None.
As huanxiner answered on ROS answers, you can use lookupNode from the rosgraph module as follows:
import rosgraph
master = rosgraph.Master("")
print master.lookupNode("node_name")
The result is something like this:
http://192.168.123.1:45678/
I am working on pdf document clustering over hadoop so I am learning mapreduce by reading some examples on internet.In wordcount examples have lines
job.get("map.input.file")
job.getboolean()
What is function of these functions?what is exactly map.input.file where is it to set? or is it just a name given to input folder?
Please post answer if anyone know.
For code see the following link
wordcount 2.0 example=http://hadoop.apache.org/docs/r1.0.4/mapred_tutorial.html
These are job configurations. i.e. set of configurations which are passed on to each mapper and reducer. Now, these configurations consist of well defined mapreduce/hadoop related configurations as well as user-defined configurations.
In your case, map.input.file is a pre-defined configuration and yes it is set to a comma separated list of all the paths you have set as input path.
While wordcount.skip.patterns is a custom configuration which is set as per user's input, and you may see this configuration to be set in run() as follows:
conf.setBoolean("wordcount.skip.patterns", true);
As for when to use get and when to use getBoolean, it should be self-explanatory, as whenever you want to set a value of type boolean you will use getBoolean and setBoolean to get and set the specific config value respectively. Similarly you have specific methods for other data types as well. If it is string then you may use get().
I am trying to use filters to select specific tables to replicate.
I tried running this with the installer
./tools/tungsten-installer --master-slave -a \
...
--svc-extractor-filters=replicate \
--property=replicator.filter.replicate.do=test,*.foo"
and got this exception in trepctl status after the master had not installed properly:
Plugin class name property is missing or null: key=replicator.filter.replicate
which file is this properties file? How do I find it? Moreover, in specifying the settings for the filter, how do I know what exactly to put?
I discovered that I am supposed to Modify the configuration template file prior to configuration according to Issue 219 but what changes am I supposed to make in tungsten-replicator-2.0.5-diff that will later on be patched to the extraction?
Issue 254 suggests that If you want to apply a filter out of the box, you can use these options with tungsten-installer:
-a --property=replicator.filter.Replicate.ignoreFilter=schema_x.tablex,schema_x,tabley,schema_y,tablez
--svc-thl-filter=Replicate
However when I try using this for --property=replicator.filter.replicate.do,
but the problem is still the same:
pendingExceptionMessage: Plugin class name property is missing or null: key=replicator.filter.replicate
Your assistance will be greatly appreciated.
Rumbi
Update:
Hi
I had a look at this file: /root/tungsten/tungsten-replicator/samples/
conf/filters/default/tableignore.tpl .Acoording to this sample, a
static-SERVICE_NAME.properties file is supposed to have something like
this configured, please confirm if this is the correct syntax:
replicator.filter.tabledo=com.continuent.tungsten.replicator.filter.JavaScr iptFilter
replicator.filter.tabledo.script=${replicator.home.dir}/samples/
scripts/javascript-advanced/tabledo.js
replicator.filter.tabledo.tables=foo(database).bar(table)
replicator.stage.thl-to-dbms.filters=tabledo
However, I did not find tabledo.js (or something similar) in the
directory where tableignore.js exists. Could I please have the
location of this file. If there is an alternative way of specifiying
--property=replicator.filter.replicate.do=test without the use of
this .js file, your suggestions are most welcome.
Download the latest version of tungsten replicator. The missing tpl file was added about a month ago. After installation, the filtered tables should be added to static-service.properties under the section FILTERS.
Locate your replicator configuration file in static-YOUR_SERVICE_NAME.properties, e.g.
/opt/continuent/tungsten/tungsten-replicator/conf/static-mysql2vertica.properties
Make sure the individual dbms properties are set, in particular the setting replicator.applier.dbms:
# Batch applier basic configuration information.
replicator.applier.dbms=com.continuent.tungsten.replicator.applier.batch.SimpleBatchApplier
replicator.applier.dbms.url=jdbc:mysql:thin://${replicator.global.db.host}:${replicator.global.db.port}/tungsten_${service.name}?createDB=true
replicator.applier.dbms.driver=org.drizzle.jdbc.DrizzleDriver
replicator.applier.dbms.user=${replicator.global.db.user}
replicator.applier.dbms.password=${replicator.global.db.password}
replicator.applier.dbms.startupScript=${replicator.home.dir}/samples/scripts/batch/mysql-connect.sql
# Timezone and character set.
replicator.applier.dbms.timezone=GMT+0:00
replicator.applier.dbms.charset=UTF-8
# Parameters for loading and merging via stage tables.
replicator.applier.dbms.stageTablePrefix=stage_xxx_
replicator.applier.dbms.stageDirectory=/tmp/staging
replicator.applier.dbms.stageLoadScript=${replicator.home.dir}/samples/scripts/batch/mysql-load.sql
replicator.applier.dbms.stageMergeScript=${replicator.home.dir}/samples/scripts/batch/mysql-merge.sql
replicator.applier.dbms.cleanUpFiles=false
Depending on the database you are replicating to you may have to omit/modify some of the lines.
For more information see:
https://code.google.com/p/tungsten-replicator/wiki/Replicator_Batch_Loading
I don't know if this problem is still open or not.
I am using this version 2.0.6-xxx and installing the service using the parameters works for me.
I would like to point it out, that as the parameter says "--svc-extractor-filters" defines an extractor filter. Meaning that the parameters will guide the extraction of data in the master server.
If you intend to use it on the slave service, you should use the "--svc-applier-filters".
The parameters
--svc-extractor-filters=replicate \
--property=replicator.filter.replicate.do=test,*.foo"
supposed to create the following in the properties file:
This is the filter set up.
replicator.filter.replicate=com.continuent.tungsten.replicator.filter.ReplicateFilter
replicator.filter.replicate.ignore=
replicator.filter.replicate.do=test,*.foo
And you should also be able to find the
replicator.stage.binlog-to-q.filters=replicate
parameter set.
If you intend to use this filter in the slave, please find the line with:
replicator.stage.q-to-dbms.filters=mysqlsessions,pkey,bidiSlave
and change it as
replicator.stage.q-to-dbms.filters=mysqlsessions,pkey,bidiSlave,replicate
Hope this brief description did help to you!
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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.