deco | A simplified parallel computing model for Python
kandi X-RAY | deco Summary
kandi X-RAY | deco Summary
deco
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Top functions reviewed by kandi - BETA
- Visits a CALL node .
- Initialize this object
- Simulates the body of a body
- Executes an internal call .
- Call function .
- Extract data set from the given data set .
- Get attribute .
- Removes leading whitespace .
- Executes the work .
- Simulate body
deco Key Features
deco Examples and Code Snippets
Lines of Code : 1License : Non-SPDX (NOASSERTION)
Copyconan remote add enhex https://api.bintray.com/conan/enhex/enhex
#!/usr/bin/env python3
"""Example for aiohttp.web basic server with decorator definition for routes."""
import textwrap
from aiohttp import web
routes = web.RouteTableDef()
@routes.get("/")
async def intro(request: web.Request) -> web.StreamR >>> class E:
... def x(self):
... return __class__ # return the __class__ cell
...
>>> E().x()
__main__.E
>>> # The cell is stored as a __closure__
>>> E.x.__closure__[0].cell_contents is E().docker run --interactive --tty \
--volume /src:/src \
--volume /data/:/root/data \
--volume /jars:/jars \
reports bash '-c' "cp -r /jars /opt/spark-3.1.1-bin-hadoop3.2/jars &aclass deco():
@staticmethod
def time_1(func):
def inner():
return 0
return inner
@staticmethod
def time_2(func):
def inner():
return 0
return inner
@deco.time_1
def test():
return 4+5
def no_multiple_inheritance(cls):
if(len(cls.__bases__)>1):
raise Exception('no multiple inheritance allowed')
return cls
def deco(cls):
print(dir(type(cls)))
print(cls.__flags__, cls.__name__)
ret# 1. get spark-3.1.1-bin-hadoop2.7.tgz from https://archive.apache.org/dist/spark/spark-3.1.1/
# (You can get different version, this one worked for me, newer might be better for you - version with log4j fix might be available now)
# 2. op(isinstance(self.good_df.schema[c].dataType, StringType))
from pyspark.sql.functions import lit
lit(isinstance(self.good_df.schema[c].dataType, StringType))
self.good_df = self.good_df.selrdd = spark.sparkContext.parallelize(comments)
rdd = spark.sparkContext.parallelize([(c,) for c in comments])
df = spark.createDataFrame([(c,) for c in comments], schema=schema)
df.show()
output_df1.createOrReplaceTempView('output_table')
def output_agg(output_table_1):
output_agg_1 = spark.sql(f"""
select * from {output_table_1}
""")
return output_agg_1
output_agg('output_table')
Community Discussions
Trending Discussions on deco
QUESTION
I want to make a decorator only works on string properties and generate error if not. Is that possible?
...ANSWER
Answered 2021-Jun-10 at 06:04type Allowed =
T[K] extends Allow // If T extends Allow
? T // 'return' T
: never; // else 'return' never
function deco () {
return function <
T, // infers typeof target
K extends keyof T // infers typeof target's key
>(
target: Allowed, // only allow when T[K] is string
key: K
) { }
}
class A {
@deco() // This should be valid
foo!: string
@deco() // This should be invalid
bar!: number
}
QUESTION
I'd like to give the static properties some initial values based on certain calculation if those properties have been decorated so I made such code:
...ANSWER
Answered 2021-Jun-10 at 01:47Since you don't want to use any, you could try something like this for the factory (inner) function, using additional method type parameters and a type intersection:
QUESTION
I have a class as follows:
...ANSWER
Answered 2021-Jun-08 at 16:58You can use a superclass and the __init_subclass__ hook to wire things up:
QUESTION
Hi I am facing a problem related to pyspark, I use df.show() it still give me a result but when I use some function like count(), groupby() v..v it show me error, I think the reason is that 'df' is too large.
Please help me solve it. Thanks!
...ANSWER
Answered 2021-May-17 at 23:57You're using a wildcard in your path '/mnt/raw_data/play/log_stream/playstats_v100/topic=play_map_play_vod/date=2021-01*', so probably one of them is corrupted. show doesn't throw any error that's mean the path of the records is shown basically correct, but not all of them. You can debug which one is causing the error by checking paths one by one (or few at the time)
QUESTION
Good day
I am writing a Glue job on AWS to transform data. After doing a join on two sets of data (resulting in a dataframe of around 100MB in size), I get a Nullpointer exception when retrieving the count on the dataframe. What makes this bug difficult to trace is that it only happens sporadically - occasionally it succeeds.
The error is:
...ANSWER
Answered 2021-May-14 at 11:28In case someone also runs into this. It can happen the moment the data gets "collected". So on writing out a partition of data, getting counts, etc.
The solution is to increase the maximum number of workers. Changing ours from 4 to 25 solved the issue.
QUESTION
The mwe of my problem I just coded:
...ANSWER
Answered 2021-May-06 at 14:51I'm quite sure what happens here is that the deco_name is interpreted before object initialisation. Pretty much what happens when you annotate a function in a class is that that decorator is "prepared" once the class itself is interpreted, meaning that @deco is unchangable by the time you assign g.
The best option would probably be to just package hello in a different function which fulfills the function of the decorator:
QUESTION
I am running below PyFlink program (copied from https://ci.apache.org/projects/flink/flink-docs-release-1.12/dev/python/table_api_tutorial.html)
...ANSWER
Answered 2021-Mar-16 at 02:07The problem is that the legacy DataSet you are using does not support the FileSystem connector you declared. You can use blink Planner to achieve your needs.
QUESTION
Our setup is configured that we have a default Data Lake on AWS using S3 as storage and Glue Catalog as our metastore.
We are starting to use Apache Hudi and we could get it working following de AWS documentation. The issue is that, when using the configuration and JARs indicated in the doc, we are unable to run spark.sql on our Glue metastore.
Here follows some information.
We are creating the cluster with boto3:
ANSWER
Answered 2021-Apr-12 at 11:46please open an issue in github.com/apache/hudi/issues to get help from the hudi community.
QUESTION
I run into an issue where a PyFlink job may end up with 3 very different outcomes, given very slight difference in input, and luck :(
The PyFlink job is simple. It first reads from a csv file, then process the data a bit with a Python UDF that leverages sklearn.preprocessing.LabelEncoder. I have included all necessary files for reproduction in the GitHub repo.
To reproduce:
conda env create -f environment.yamlconda activate pyflink-issue-call-already-closed-envpytestto verify the udf defined inml_udfworks finepython main.pya few times, and you will see multiple outcomes
There are 3 possible outcomes.
Outcome 1: success!It prints 90 expected rows, in a different order from outcome 2 (see below).
Outcome 2: call already closedIt prints 88 expected rows first, then throws exceptions complaining java.lang.IllegalStateException: call already closed.
ANSWER
Answered 2021-Apr-16 at 09:32Credits to Dian Fu from Flink community.
Regarding outcome 2, it is because the input date (see below) has double quotes. Handling the double quotes properly will fix the issue.
QUESTION
I'm trying to repeatedly call a decorated function from within another class, such that the decorator is executed everytime the function is called.
The original question is below but does not explicitly relate to pyqt as pointed out correctly.
I'm trying to use decorators within a pyqt thread. From how I understand decorators, the decoration should be executed every time the function is called. (Or at least that is what I want.) However, calling a decorated function from within a pyqt thread leads to execution of the decorator only once.
This is my tested example:
...ANSWER
Answered 2021-Apr-15 at 13:46You could use function decorator instead:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install deco
You can use deco like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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