10. Additional Tutorials¶
10.1. A PNG to JPEG conversion plugin¶
Let’s create an plugin to convert a PNG image to a JPEG image and then to archive the JPEG image.
First, we have to modify the archive_image
plugin (see Use of the ungzip plugin tutorial) to be able to archive all images and not only PNG images.
Open the config.ini
file of the archive_image
plugin and change the switch_logical_condition
to accept all images:
switch_logical_condition = ( b'image' in x['latest.switch.main.system_magic'] )
Now, create the converter plugin from the MFDATA fork
template which allows to execute shell commands in a subprocess. Enter the command:
bootstrap_plugin.py create --template=fork convert_png
Configure the switch_logical_condition
parameter in the convert_png/config.ini
that allow to convert PNG files.
switch_logical_condition = ( x['latest.switch.main.system_magic'].startswith(b'PNG image') )
Then, let’s create a script file (convert.sh
in the convert_png
plugin directory) to convert a PNG file to a JPEG file (we will use the ImageMagick convert tool):
#!/bin/bash
# $1 is the incoming PNG file.
# Convert to JPEG
convert "$1" "$1.jpeg"
# Re-inject the converted file to the switch plugin
inject_file --plugin=switch "$1.jpeg"
Set convert.sh
as an executable file:
chmod +x convert.sh
Now, we have to say to the convert_png
plugin to launch the convert.sh
script for each file. To do this, configure the arg_command-template
parameter in the convert_png/config.ini
:
arg_command-template = {PLUGIN_DIR}/convert.sh {PATH}
Caution
The arg_command-template command must NOT be enclosed by quotation marks:
INVALID:
arg_command-template = "{PLUGIN_DIR}/convert.sh {PATH}"
arg_command-template = '{PLUGIN_DIR}/convert.sh {PATH}'
VALID
arg_command-template = {PLUGIN_DIR}/convert.sh {PATH}
Then, install (as dev build) the plugin by entering the command make develop
from the convert_png
plugin directory.
Check the plugin is installed, by running plugins.list
.
Run the plugin: inject a PNG compressed file :
inject_file --incoming /tmp/my_png_file.png.gz
Check the archive directory. Go to the /tmp/my_archive_image/[YYYYMMDD]
directory where [YYYYMMDD]
is the current date.You will see two files, a PNG one and a JPEG one, with a ‘RANDOM_ID’ name: e.g. 85c87fe07e604c01bb81ba8311611466
and c210be74b8644aeeb6c1d41e4883649d
:
file 85c87fe07e604c01bb81ba8311611466
85c87fe07e604c01bb81ba8311611466: PNG image data, 120 x 165, 8-bit/color RGBA, non-interlaced
file c210be74b8644aeeb6c1d41e4883649d
c210be74b8644aeeb6c1d41e4883649d: JPEG image data, JFIF standard 1.01
The diagram below shows the data flow:
- The GZIP file is processed by the
switch
plugin from the MFDATAincoming
directory - The
gunzip
plugin uncompress the file - The
gunzip
plugin puts the PNG file in theincoming
directory. It will be processed by theswitch
plugin - The
archive_image
plugin processes the PNG file (itsswitch_logical_condition
isTrue
). Theconvert_image
plugin processes the PNG file (itsswitch_logical_condition
isTrue
). - The
convert_image
plugin puts (injects) the JPEG file in theincoming
directory throw theswitch
plugin (inject_file --plugin=switch "$1.jpeg"
command in theconvert.sh
script). It will be processed by theswitch
plugin. - The
archive_image
plugin processes the JPEG file (itsswitch_logical_condition
isTrue
).
10.2. Sending a file by FTP¶
Let’s now create an plugin from the ftpsend
MFDATA template.
This new plugin aims to send a JPEG file to a FTP host.
Use the ftpsend
template to create the plugin : run the following command:
bootstrap_plugin.py create --template=ftpsend ftpsend_to_mybox
Enter the machine
, user
and passwd
when prompting, respectively the destination host, user and password (press [Enter] for the other parameters to keep the default value).
Go to the ftpsend_to_mybox
sub-directory, open the config.ini
file and check, in the [step_send]
section, the parameters you have just entered:
# machine : target machine for ftp transfer
arg_machine = mybox
# user : target user for ftp transfer
arg_user = myuser
# passwd : target passwd for ftp transfer
arg_passwd = mypassword
Then, install (as dev build) the plugin by entering the command make develop
from the ftpsend_to_mybox
plugin directory.
Check the plugin is installed, by running plugins.list
.
Let’s now change the convert_png
plugin to feed the new ftpsend_to_mybox
plugin.
Open the convert_png/convert.sh
script file and change the inject_file
command as below in the convert_png
plugin directory) to convert a PNG file to a JPEG file (we will use the ImageMagick convert tool):
#!/bin/bash
# $1 is the incoming PNG file.
# Convert to JPEG
convert "$1" "$1.jpeg"
# Re-inject the converted file to the switch plugin
inject_file --plugin=ftpsend_to_mybox --step=send "$1.jpeg"
Tip
The ftpsend template contains two steps (send`and `reinject). It has no main (default) step. That’s why we specify the step to execute in the inject_file command.
The diagram below shows the data flow:
- The GZIP file is processed by the
switch
plugin from the MFDATAincoming
directory - The
gunzip
plugin uncompress the file - The
gunzip
plugin puts the PNG file in theincoming
directory. It will be processed by theswitch
plugin - The
archive_image
plugin processes the PNG file (itsswitch_logical_condition
isTrue
). Theconvert_image
plugin processes the PNG file (itsswitch_logical_condition
isTrue
). - The
convert_image
plugin puts (injects) the JPEG file to theftpsend_to_mybox
plugin (inject_file --plugin=ftpsend_to_mybox --step=send "$1.jpeg""
command in theconvert.sh
script). It will be processed by theftpsend_to_mybox
plugin. - The
ftpsend_to_mybox
plugin sends the JPEG file to “mybox”.
10.3. Create a plugin from scratch¶
This tutorial is designed to help you get started with MFDATA plugin from scratch, i.e. without any MFDATA template.
Let’s suppose we want to create a plugin to convert a GRIB file into a NetCDF file.
In this tutorial:
- we will use:
- the
grib_to_netcdf
command from eccodes (see https://confluence.ecmwf.int/display/ECC) and available in the Metwork MFEXT ‘scientific’ package - the
NetCDF4
Python library (see http://unidata.github.io/netcdf4-python/)
- the
- we will call
grib_to_netcdf
command from Python code (instead of a shell script). - we will save the NetCDF file in a specific directory
- we will save the “tags attributes” set by the switch plugin to a “tags” file in the same directory as the NetCDF file one
- we will read some data of the NetCDF file from Python code with the
NetCDF4
Python library
Important:
if you are behind a proxy, you have to set
http_proxy
andhttps_proxy
environment varaibles in order to be able to download any Python package you may need.you may also need to disable your Linux firewall:
systemctl status firewalld systemctl stop firewalld.service systemctl disable firewalld
10.3.1. Create the plugin¶
In order to create the plugin : run the following command:
bootstrap_plugin.py create convert_grib2
By default, a main.py
Python script is created in the convert_grib2
directory. It corresponds to the main
step of the plugin:
#!/usr/bin/env python3
from acquisition.step import AcquisitionStep
class Convert_grib2MainStep(AcquisitionStep):
plugin_name = "convert_grib2"
step_name = "main"
def process(self, xaf):
self.info("process for file %s" % xaf.filepath)
return True
if __name__ == "__main__":
x = Convert_grib2MainStep()
x.run()
It contains a derived class Convert_grib2MainStep
that inherits of the
AcquisitionStep
class.
The most important method is process
which overrides the process
method of the base class AcquisitionStep
.
The process
method is called in the run
method of the AcquisitionStep
class.
The xaf
parameter (from XattrFile
class) is the file to be processed.
10.3.2. Set dependencies¶
To build the plugin, we needs the eccodes
programs and libraries (see https://confluence.ecmwf.int/display/ECC) provided in the MFEXT ‘scientific’ package.
So, the MFEXT ‘scientific’ package must be installed. To check this, from the /home/mfdata/
directory, just enter the grib_to_netcdf
command:
grib_to_netcdf --help
For further about grib_to_netcdf
, see https://confluence.ecmwf.int/pages/viewpage.action?pageId=23693254
Tell the plugin to use MFEXT ‘scientific’ package. Edit the .layerapi2_dependencies
in the convert_grib2
directory and add the python3_scientific@mfext
at the end:
python3@mfdata
python3_scientific@mfext
As we will use the NetCDF4
Python library, we have to add this dependency to the ‘Requirements’ file (for more details on this topic, see https://pip.readthedocs.io/en/1.1/requirements.html). Edit the python3_virtualenv_sources/requirements-to-freeze.txt
file and add the following line:
NetCDF4
If you don’t mention any version of the library, the latest available version will be use.
You may mention a specific version:
NetCDF4==1.4.2
Check the dependencies settings by entering the command make develop
from the convert_grib2
plugin directory.
10.3.3. Fill in the plugin¶
Let’s now add Python code into the plugin to convert input GRIB file to NetCDF.
Create a grib_to_netcdf_command
Python method which builds and runs the ecCodes grib_to_netcdf
command:
#!/usr/bin/env python3
import re
import subprocess
....
class Convert_grib2MainStep(AcquisitionStep):
...
def grib_to_netcdf_command(self, grib_file_path, netcdf_file_path):
"""
Convert GRIB file to Netcdf File
:param grib_file_path: GRIB file path to convert
:param netcdf_file_path: output NetCDF file path to convert
:raise: Exception if something wrong happens
"""
# Build the 'grib_to_netcdf' command
command_grib_to_netcdf = list()
command_grib_to_netcdf.append("grib_to_netcdf")
command_grib_to_netcdf.append(grib_file_path)
command_grib_to_netcdf.extend("-k 3 -d 0 -D NC_FLOAT".split(' '))
command_grib_to_netcdf.append("-o")
command_grib_to_netcdf.append(netcdf_file_path)
self.debug(command_grib_to_netcdf)
try:
# Run the the 'grib_to_netcdf' command
result_grib_to_netcdf = subprocess.check_call(command_grib_to_netcdf)
if result_grib_to_netcdf != 0:
msg = 'Unable to execute command {}. Result is: {}.'.format(command_grib_to_netcdf,
result_grib_to_netcdf)
raise Exception(msg)
except subprocess.CalledProcessError as e:
msg = 'Unable to execute command {}. Reason: {}'.format(command_grib_to_netcdf, str(e))
raise Exception( msg, e)
Now, we need to set the destination directory where the NetCDF files will be stored. In order to to this, we add an argument (parameter) in the section [step_main]
of our config/config.ini
plugin file:
[step_main]
....
# Destination directory of the converted NetCDF files
arg_netcdf-dest-dir = /tmp/my_netcdf
....
Important
- the parameter must always be prefixed by arg_. Then, use ‘-‘ and not ‘_’ in your parameter: arg_netcdf-dest-dir is valid, but arg_netcdf_dest_dir is NOT valid.
- the argument parameter as Python variable will be self.args.netcdf_dest_dir.
Then, we must override the add_extra_arguments
method in order to parse our netcdf_dest-dir
argument:
class Convert_grib2MainStep(AcquisitionStep):
...
def add_extra_arguments(self, parser):
# Call the parent add_extra_arguments
super().add_extra_arguments(parser)
parser.add_argument('--netcdf-dest-dir', action='store',
default=None,
help='Netcdf destination directory')
...
We have to check netcdf-dest-dir
argument is set and create the destination directory. We do this in the init
method:
...
from mfutil import mkdir_p_or_die
...
class Convert_grib2MainStep(AcquisitionStep):
...
def init(self):
super().init()
if self.args.netcdf_dest_dir is None:
raise Exception('you have to set a netcdf-dest-dir')
# Create dthe destination directory
mkdir_p_or_die(self.args.dest_dir)
...
We are only interested in GRIB file.
By default, the Linux magic
file doesn’t contain any GRIB file identification.
So, we need to create a magic
file in the root directory of the convert_grib2
plugin (for further about magic
, see Identify particular types of files.
Create a new magic
file in your plugin directory and add grib
identification rules:
# GRIB
0 string GRIB GRIB file
Caution
The magic file must be named magic and must be stored in the plugin root directory (i.e., here, convert_grib2 directory).
Then, set the switch_logical_condition
to accept only GRIB file:
switch_logical_condition = (x['latest.switch.main.convert_grib2_magic'].startswith(b'GRIB file'))
Tip
Because we create a custom magic file, the condition must be set on latest.switch.main.convert_grib2_magic instead of latest.switch.main.system_magic:
# CORRECT
switch_logical_condition = (x['latest.switch.main.convert_grib2_magic'].startswith(b'GRIB file'))
# BAD
switch_logical_condition = (x['latest.switch.main.system_magic'].startswith(b'GRIB file'))
Fill in the process
method:
...
import os
...
def process(self, xaf):
"""
This function:
- Convert a GRIB file into a NetCDF file.
- Read some data of the NetCDF file
:param xaf: the input GRIB data file as an XattrFile object
:return: True, if the process is successful, False, if the process failed
"""
# xaf.filepath is the internal file name created by the switch plugin into a temporary directory
self.info("process for file %s" % xaf.filepath)
try:
# In order to get the original GRIB file name, call AcquisitionStep.get_original_basename
original_grib_filename = str(AcquisitionStep.get_original_basename(self, xaf))
# Build the output NetCDF file name from the input file name,
netcdf_filename = re.sub(r'(\.grb|\.grib2|\.grib)$', '', str(original_grib_filename)) + ".nc"
netcdf_filepath = os.path.normpath(os.path.join(self.args.netcdf_dest_dir, netcdf_filename))
# Convert Grib to Netcdf
self.grib_to_netcdf_command(xaf.filepath, netcdf_filepath)
except Exception as e:
self.exception(str(e))
return False
return True
Then, install (as dev build) the plugin by entering the command make develop
from the convert_grib2
plugin directory.
Check the plugin is installed, by running plugins.list
.
Run the plugin: inject a GRIB file :
inject_file --incoming /tmp/my_grib_file.grib2
Check the NetCDF file have been created and stored in the netcdf-dest-dir
directory. You mays check the content of the NetCDF file through th ncdump
commands (available in MFEXT package):
ncdump -h /tmp/my_netcdf/my_grib_file.nc
You mays also check the logs in the step_convert_grib2_main.stdout
and step_convert_grib2_main.stderr
in the /home/mfdata/log
directory.
Did the conversion fail ? Possible reasons are due to some resource limits configured in ``config/config.ini`` file of the plugin. In order to fix these issues, you have to increase the resouce limits, especially the rlimit_as
and/or rlimit_fsize
parameter:
...
# resource limit for each step process
# rlimit_as => maximum area (in bytes) of address space which may be taken by the process.
# rlimit_nofile => maximum number of open file descriptors for the current process.
# rlimit_stack => maximum size (in bytes) of the call stack for the current process.
# This only affects the stack of the main thread in a multi-threaded process.
# rlimit_core => maximum size (in bytes) of a core file that the current process can create.
# rlimit_fsize => maximum size of a file which the process may create.
# (empty value means no limit)
#
rlimit_as = 10000000000
rlimit_nofile = 1000
rlimit_stack = 10000000
rlimit_core = 10000000000
rlimit_fsize = 10000000000
After editing the config.ini
file, just rebuild the plugin with the make develop
command.
Now, we add some code to store tags/attributes in an additional file.
Edit the convert_grib2/config.ini
and add the following configuration:
# Keep tags/attributes in an additional file
arg_keep_tags = True
# If keep_tags=1, the suffix to add to the filename to store tags
arg_keep_tags_suffix = .tags
Edit the main.py
Python script and add some code in add_extra_arguments
and process
methods so that it to look like this:
...
from xattrfile import XattrFile
...
def add_extra_arguments(self, parser):
# Call the parent add_extra_arguments
super().add_extra_arguments(parser)
parser.add_argument('--netcdf-dest-dir', action='store',
default=None,
help='Netcdf destination directory')
parser.add_argument('--keep-tags', action='store',
type=bool, default=True,
help='keep tags/attributes into another file ?')
parser.add_argument('--keep-tags-suffix', action='store',
default=".tags",
help='if keep-tags=True, suffix to add to the '
'filename to keep tags')
...
def process(self, xaf):
"""
This function:
- Convert a GRIB file into a NetCDF file.
- Read some data of the NetCDF file
:param xaf: the input GRIB data file as an XattrFile object
:return: True, if the process is successful, False, if the process failed
"""
# xaf.filepath is the internal file name created by the switch plugin into a temporary directory
self.info("process for file %s" % xaf.filepath)
try:
# In order to get the original GRIB file name, call AcquisitionStep.get_original_basename
original_grib_filename = str(AcquisitionStep.get_original_basename(self, xaf))
# Build the output NetCDF file name from the input file name,
netcdf_filename = re.sub(r'(\.grb|\.grib2|\.grib)$', '', str(original_grib_filename)) + ".nc"
netcdf_filepath = os.path.normpath(os.path.join(self.args.netcdf_dest_dir, netcdf_filename))
# Convert Grib to Netcdf
self.grib_to_netcdf_command(xaf.filepath, netcdf_filepath)
# We tags/attributes in a specific file
if self.args.keep_tags:
tags_filepath = netcdf_filepath + self.args.keep_tags_suffix
xaf.write_tags_in_a_file(tags_filepath)
XattrFile(netcdf_filepath).clear_tags()
except Exception as e:
self.exception(str(e))
return False
return True
Build the plugin (make develop
) and inject your GRIB file again.
Check a .tags
file has been created next to the NetCDF file in the netcdf-dest-dir
directory. The content of the .tags
file looks like:
0.switch.main.convert_grib2_magic = GRIB file
0.switch.main.enter_step = 2019-03-20T11:30:41:946203
0.switch.main.exit_step = 2019-03-20T11:30:42:006251
0.switch.main.process_status = ok
0.switch.main.system_magic = data
1.convert_grib2.main.enter_step = 2019-03-20T11:30:42:018932
first.core.original_basename = AROME_SP1_201812030600.grib2
first.core.original_dirname = incoming
first.core.original_uid = 463d0dc5826a4143a427d0b6b1e3245f
latest.core.step_counter = 1
latest.switch.main.convert_grib2_magic = GRIB file
latest.switch.main.system_magic = data
Now, we are going to learn another way to configure a plugin.
We are going to improve our plugin to be able to customize the configuration of the grib_to_netcdf
command. As we saw previously, the grib_to_netcdf
command has optional parameter, we hard-coded (i.e "-k 3 -d 0 -D NC_FLOAT"
).
We could set an arg_grib-to-netcdf-options
parameter in the convert_grib2/config.ini
, but here, we set this parameter in the MFDATA configuration file /home/mfdata/config.ini
. This configuration file can contain a section per plugin. The secton name must be named [plugin_{plugin_name}]
. Edit /home/mfdata/config.ini
and add the following section and parameter at the ne of the file:
[plugin_convert_grib2]
grib_to_netcdf_options=-k 3 -d 0 -D NC_FLOAT
Each parameter will be will transform into an environment variable whose pattern is {MODULE}_{SECTION_NAME}_{PARAMETER_NAME}
, e.g. MFDATA_PLUGIN_CONVERT_GRIB2_GRIB_TO_NETCDF_OPTIONS
Note
- Environment variables are always in uppercase.
- To get the new value, you have to close/reopen your terminal to force a new profile loading.
- To change daemons and services behaviour (like nginx listening port in your example), you have to restart services from a newly restarted terminal or from a root user through service metwork restart command.
For more details, see How to configure mfdata metwork module ?.
Then, if you enter :
env | grep "^${MODULE}_" | grep CONVERT
You should see something like this:
MFDATA_PLUGIN_CONVERT_GRIB2_GRIB_TO_NETCDF_OPTIONS=-k 3 -d 0 -D NC_FLOAT
Adjust the main.py
code.
Add a __init__
method in order to get the grib_to_netcdf_options
from MFDATA_PLUGIN_CONVERT_GRIB2_GRIB_TO_NETCDF_OPTIONS
envronment variable:
def __init__(self):
self.grib_to_netcdf_options_default = "-k 3 -d 0 -D NC_FLOAT"
self.grib_to_netcdf_options = os.environ.get("MFDATA_PLUGIN_CONVERT_GRIB2_GRIB_TO_NETCDF_OPTIONS",
self.grib_to_netcdf_options_default)
Adjust the grib_to_netcdf_command
method by replacing the hard-coded grib_to_netcdf options:
def grib_to_netcdf_command(self, grib_file_path, netcdf_file_path):
"""
Convert GRIB file to Netcdf File
:param grib_file_path: GRIB file path to convert
:param netcdf_file_path: output NetCDF file path to convert
:raise: Exception if something wrong happens
"""
# Build the 'grib_to_netcdf' command
command_grib_to_netcdf = list()
command_grib_to_netcdf.append("grib_to_netcdf")
command_grib_to_netcdf.append(grib_file_path)
command_grib_to_netcdf.extend(self.grib_to_netcdf_options.split(' '))
command_grib_to_netcdf.append("-o")
command_grib_to_netcdf.append(netcdf_file_path)
...
CAUTION:
You could be tempted to declare the grib_to_netcdf_options
as a class attribute instead of instance attribute, e.g.:
class Convert_grib2MainStep(AcquisitionStep):
plugin_name = "convert_grib2"
step_name = "main"
grib_to_netcdf_options_default = "-k 3 -d 0 -D NC_FLOAT"
grib_to_netcdf_options = os.environ.get("MFDATA_PLUGIN_CONVERT_GRIB2_GRIB_TO_NETCDF_OPTIONS",
self.grib_to_netcdf_options_default)
def __init__(self):
pass
...
This works, but you have to relead the context at the first time and ecach time the variable value is changed in the /home/mfdata/config.ini
, by stopping en starting again MFDATA:
mfdata.stop
then
mfdata.start
We will now use the NetCDF4
library to read the NetCDF file.
Add the following instructions in the Convert_grib2MainStep
class and the process
method:
...
from netCDF4 import Dataset
...
def process(self, xaf):
...
try:
...
# Read the output NetCDF
# Log the dimensions name and variable names
netcdf_dataset = Dataset(netcdf_filepath, "r")
self.info("Dimensions of the Netcdf dataset {}:".format(netcdf_filepath))
for dim_name in netcdf_dataset.dimensions:
self.info(dim_name)
self.info("Variables of the Netcdf dataset {}:".format(netcdf_filepath))
for var_name in netcdf_dataset.variables:
self.info(var_name)
Build the plugin (make develop
) and inject your GRIB file again.
Check the the logs file log/step_convert_grib2_main.stdout
.
10.3.4. Override a configuration parameter¶
We are going to see how to override a configuration parameter in a plugin (inheritance).
In our plugin, in case of execution failure, we would like to move the input GRIB file to a specific directory (e.g. /tmp/convert-grib2-failure
). In order to do his , we will use the failure-policy
and failure-policy-move-dest-dir
options defined in the base class AcquisitionStep
.
The default value of failure-policy
is keep
.
The expected value for are
- delete => we delete the file
- keep => we keep the file in trash
- move => we move the file in another directory. In this case, the
failure-policy-move-dest-dir
must be set.
Edit the convert_grib2/config.ini
file and add in the [step_main]
section:
arg_failure-policy = move
arg_failure-policy-move-dest-dir = /tmp/convert-grib2-failure
So, we have to override the value to move
.
To be bable to raise an error in our plugin process, you may decrease the value of the rlimit_fsize
option in the [step_main]
section of the convert_grib2/config.ini
file, e.g.:
...
# rlimit_fsize => maximum size of a file which the process may create.
rlimit_fsize = 100000
Build the plugin (make develop
) and inject your GRIB file again.
You should see in the logs file log/step_convert_grib2_main.stderr
the grib_to_netcdf
command fails.
Check the input file has been moved to the /tmp/convert-grib2-failure
.
Now, you may reset rlimit_fsize
value to your previous value (e.g. 1000000000)
Note
In this tutorial, another way to trigger the failure policy is to always force a return False in the process method.
For further about overriding a configuration option, refer to How to configure mfdata metwork module ?.
10.3.5. Using the after
callback¶
An after
callback is called after the process
method execution whether the method failed or not.
If you consider performing some actions after the process
execution, just define (ovveride) the after
method in the Convert_grib2MainStep
class, e.g.:
def after(self, status):
"""
Method called after the process execution
:param status: status of the process execution
"""
self.info("GRIB to NetCDF conversion ended with status {}".format(status))
Full convert_grib2 Python example
.
10.4. Creating a batch plugin¶
This tutorial is designed to help you to create a plugin which allow to process several files from 1 to max_batch_size
in one call.
Create a plugin from scratch (as explained in the Create a plugin from scratch tutorial:
bootstrap_plugin.py create batch_tuto
Go to the batch_tuto
plugin directory, edit the main.py
Python script and change:
- the base class
AcquisitionStep
byAcquisitionBatchStep
- the
process
method bybatch_process
method
as below:
#!/usr/bin/env python3
from acquisition.batch_step import AcquisitionBatchStep
class Batch_tutoMainStep(AcquisitionBatchStep):
plugin_name = "batch_tuto"
step_name = "main"
def batch_process(self, xafs):
results = []
for xaf in xafs:
file_name = self.get_original_basename(xaf)
self.info("batch process for file {} (original name is: {}".format(xaf.filepath, file_name))
results.append(True)
return results
if __name__ == "__main__":
x = Batch_tutoMainStep()
x.run()
So, the Batch_tutoMainStep
class inherits of the AcquisitionBatchStep
class.
The most important method is batch_process
which overrides the batch_process
method of the base class AcquisitionBatchStep
.
The batch_process
method is called in the run
method of the AcquisitionStep
class.
The xafs
parameter is an array containing the files to be processed, each item is an XattrFile
instance.
If the batch_process
method returns:
True
: the processing is considered to be successful for each processed fileFalse
: the processing is considered to be failed for each processed file- an array of
boolean
with the same than thexafs
array: the processing status is considered for each file.
The main arguments of a batch
plugin are:
- batch-process-max-size: the maximum number of files before launching
batch_process
. Default value is 100 (files). - batch-process-max-wait: the maximum waiting time, in seconds, before launching
batch_process
(when the batch_process_max_size is not reached). Default value is 10 (seconds).
So, the batch_process
method is called when, at least, one of the two conditions is true:
- the maximum of files to process is reached
- the mximum of time to wait is reached
If the default values don’t suit you, you may override the batch_process_max_size
and batch_process_max_wait
properties in the main.py
Python script:
...
class Batch_tutoMainStep(AcquisitionBatchStep):
...
@property
def batch_process_max_size(self):
"""Return the max size of a batch in batch process mode.
"""
return 5
@property
def batch_process_max_wait(self):
"""Max wait (in seconds) to fill the batch in batch process mode.
"""
return 60
...
You may use an more dynamic way to override the batch_process_max_size
and batch_process_max_wait
properties:
edit the
[step_main]
section of thebacth_tuto/config.ini
file and add:# batch-process-max-size : maximum number of files before launching batch process. # Default is : '100' arg_batch-process-max-size = 5 # batch-process-max-wait : maximum time before launching batch process (in seconds) # Default is : '10' (seconds) arg_batch-process-max-wait = 60
In the
main.py
Python script:- ovverride the
add_extra_arguments
- change the
batch_process_max_size
andbatch_process_max_wait
properties - log the
batch_process_max_size
andbatch_process_max_wait
properties
- ovverride the
from acquisition.batch_step import AcquisitionBatchStep
class Batch_tutoMainStep(AcquisitionBatchStep):
plugin_name = "batch_tuto"
step_name = "main"
def add_extra_arguments(self, parser):
parser.add_argument('--batch-process-max-size', action='store', default='100',
help='Max number of files before launching batch process')
parser.add_argument('--batch-process-max-wait', action='store', default='10',
help='Max time before launching batch process')
@property
def batch_process_max_size(self):
"""Return the max size of a batch in batch process mode.
"""
return int(self.args.batch_process_max_size)
@property
def batch_process_max_wait(self):
"""Max wait (in seconds) to fill the batch in batch process mode.
"""
return int(self.args.batch_process_max_wait)
def batch_process(self, xafs):
self.info("batch process batch_process_max_size: {}".format(self.batch_process_max_size))
self.info("batch process batch_process_max_wait: {}".format(self.batch_process_max_wait))
self.info("batch process xafs size: {}".format(len(xafs)))
results = []
for xaf in xafs:
file_name = self.get_original_basename(xaf)
self.info("batch process for file {} (original name is: {}".format(xaf.filepath, file_name))
results.append(True)
return results
if __name__ == "__main__":
x = Batch_tutoMainStep()
x.run()
In this tutorial, we will accept all incoming files by setting the switch_logical_condition
to True
in the [step_main]
section of the bacth_tuto/config.ini
file:
switch_logical_condition = True
Then, install (as dev build) the plugin by entering the command make develop
from the bacth_tuto
plugin directory.
Check the plugin is installed, by running plugins.list
.
Run the plugin: inject a file :
inject_file --incoming {my_file}
Then, you should see in the logs ~/log/step_batch_tuto_main.stdout
the file is processed after the batch-process-max-wait
is reached.
Then, run again the plugin by injecting more than the batch-process-max-size
number of files.
Let’s assume, arg_batch-process-max-size = 5
and arg_batch-process-max-wait = 60
. Inject 7 files in the incomin MFDATA directory (repeat command inject_file --incoming {my_file}
7 times or use cp {files} /home/mfdata/var/in/incoming/
).
Then, check the logs ~/log/step_batch_tuto_main.stdout
. You will see the batch_process
is launched after the 5th file is injected. Then, wait 60 seconds, check the logs again and you will see the last two files are processed 60 seconds later.
You could also build and check the Sending a file by FTP tutorial which implements a AcquisitionBatchStep
base class (see send.py
Python script).
10.5. Implement custom monitoring and metrics in a plugin¶
In this tutorial, we will learn how to implement some metrics in a plugin and how to show them in a monitoring dashboard.
What we want to measure is the elapsed time to convert a GRIB file to a NetCDF file in the ‘grib_to_netcdf_command’ function of the Create a plugin from scratch tutorial.
10.5.1. Add metrics in the Python code of the plugin¶
In order to do this, we will use the get_stats_client
method of the base class AcquisitionStep
.
The get_stats_client
method returns a AcquisitionStatsDClient
instance.
In order to create metrics, we just have to call the timer
timer
method from AcquisitionStatsDClient
instance. It returns an
timer instance. For further details, you may check StatsD documentation and its Api Reference.
Let’s add our metrics to the grib_to_netcdf_command
function. Edit the main.py
file and add the instructions as below:
def grib_to_netcdf_command(self, grib_file_path, netcdf_file_path):
"""
Convert GRIB file to Netcdf File
:param grib_file_path: GRIB file path to convert
:param netcdf_file_path: output NetCDF file path to convert
:raise: Exception if something wrong happens
"""
# ADD THESE LINES
#------------------
# Create timer called 'grib_to_netcdf_metrics'
timer = self.get_stats_client().timer("grib_to_netcdf_metrics")
# timer object to start counting
timer.start()
#------------------
# Build the 'grib_to_netcdf' command
command_grib_to_netcdf = list()
command_grib_to_netcdf.append("grib_to_netcdf")
command_grib_to_netcdf.append(grib_file_path)
command_grib_to_netcdf.extend(self.grib_to_netcdf_options.split(' '))
command_grib_to_netcdf.append("-o")
command_grib_to_netcdf.append(netcdf_file_path)
self.debug(command_grib_to_netcdf)
try:
# Run the the 'grib_to_netcdf' command
result_grib_to_netcdf = subprocess.check_call(command_grib_to_netcdf)
if result_grib_to_netcdf != 0:
msg = 'Unable to execute command {}. Result is: {}.'.format(command_grib_to_netcdf,
result_grib_to_netcdf)
raise Exception(msg)
# ADD THESE LINES
#------------------
# Increment a counter 'bytes_of_output_netcdf_files' with the size of the output Netcdf (in bytes)
self.get_stats_client().incr("bytes_of_output_netcdf_files", os.path.getsize(netcdf_file_path))
#------------------
except subprocess.CalledProcessError as e:
msg = 'Unable to execute command {}. Reason: {}'.format(command_grib_to_netcdf, str(e))
raise Exception(msg, e)
# ADD THESE LINES
#------------------
finally:
# stop timing and send the results
timer.stop()
Then, build the plugin by entering the command make develop
from the convert_grib2
plugin directory.
10.5.2. Create a specific dashboard with our metrics¶
Now, we have to ‘connect’ MFDATA to MFADMIN for monitoring and open MFADMIN Grafana GUI Interface from a browser. In order to do this, refer to Monitoring and Dashboards.
Now, we will create a specific dashboard with the following metrics:
- The
grib_to_netcdf_metrics
metrics will measure the duration of the GRIB to Netcdf conversion. - The
bytes_of_output_netcdf_files
metrics will measure the size of the NetCDF output file. - The
bytes_of_processed_files
metrics, created in the base classAcquisitionStep
, will measure the size of the input file.
From the Grafana Interface, create a new dashboard (click +
then dashboard
):
Then, select Graph
:
We are going to create a graph that display the mean duration of the GRIB to NetCDF conversion, i.e. the grib_to_netcdf_metrics
, we created in the Python code above.
Edit the dashboard, from Panel title
, select Edit
:
Enter the title from the General Tab:
Enter query data from the Metrics Tab, as below:
Enter axes data from the Axes Tab, as below:
Then, Legend Tab:
Display Tab:
Then save the dashboard:
Then add a new graph in our dashboard that display the sum of the input and output files sizes, i.e. the bytes_of_output_netcdf_files
and bytes_of_processed_files
metrics:
Enter data as below:
Save the dashboard.
10.5.3. View metrics on the dashboard¶
Then, run the convert_grib2
plugin by injecting some GRIB files.
You may download this GRIB file example
. You may also download more GRIB files from this site.
Then, from the MFADMIN Grafana GUI Interface, open your dashboard and you should see some the metrics displayed on the graphs.
You may also change the time ranges, by selecting a prédefined range or entering your custom range:
You may automatically refresh the graph:
10.5.4. Useful links and Tips¶
Check Useful links and Tips from the Tuning, Monitoring and Dashboard documentation.