Welcome to cloudreactor-procwrapper documentation!¶
Installation & Usage
Project Info
- Changelog
- v2.0.0-rc1 (2021-01-26)
- v2.0.0-rc2 (2021-01-31)
- v2.0.0-rc3 (2021-02-03)
- v2.0.0-rc4 (2021-02-16)
- v2.0.0-rc5 (2021-02-16)
- v2.0.0-rc6 (2021-02-21)
- v2.0.0 (2021-02-22)
- v2.1.0-rc1 (2021-03-22)
- v2.1.0-rc2 (2021-04-03)
- v2.1.0 (2021-04-03)
- v2.1.1 (2021-04-07)
- v3.0.0 (2021-07-18)
- v3.0.1 (2021-08-07)
- v3.1.0 (2021-11-11)
- v3.1.1 (2021-11-11)
- v3.1.2 (2021-11-11)
- v4.0.0-alpha1 (2022-05-27)
- v4.0.0 (2022-05-30)
- v4.0.1 (2022-06-01)
- v4.0.2 (2022-06-02)
- v5.0.0 (2022-07-22)
- v5.0.1 (2022-10-05)
- v5.0.2 (2022-10-05)
- v5.1.0 (2023-09-19)
- Contributing
cloudreactor-procwrapper¶
Wraps the execution of processes so that an API server (CloudReactor) can monitor and manage them. Available as a standalone executable or as a python module.
Features¶
Runs either processes started with a command line or a python function you supply
Implements retries and time limits
Injects secrets from AWS Secrets Manager, AWS S3, or local files and extracts them into the process environment (for command-lines) or configuration (for functions)
When used with the CloudReactor service:
Reports when a process/function starts and when it exits, along with the exit code and runtime metadata (if running in AWS ECS, AWS Lambda, or AWS CodeBuild)
Sends heartbeats, optionally with status information like the number of items processed
Prevents too many concurrent executions
Stops execution when manually stopped in the CloudReactor dashboard
Sends CloudReactor the data necessary to start the process / function if running in AWS ECS, AWS Lambda, or AWS CodeBuild
How it works¶
First, secrets and other configuration are fetched and resolved from providers like AWS Secrets Manager, AWS S3, or the local filesystem.
Just before your code runs, the module requests the API server to create a Task Execution associated with the Task name or UUID which you pass to the module. The API server may reject the request if too many instances of the Task are currently running, but otherwise records that a Task Execution has started. The module then passes control to your code.
While your code is running, it may report progress to the API server, and the API server may signal that your Task stop execution (due to user manually stopping the Task Execution), in which case the module terminates your code and exits.
After your code finishes, the module informs the API server of the exit code or result. CloudReactor monitors Tasks to ensure they are still responsive, and keeps a history of the Executions of Tasks, allowing you to view failures and run durations in the past.
Auto-created Tasks¶
Before your Task is run (including this module), the AWS ECS CloudReactor Deployer can be used to set it up in AWS ECS, and inform CloudReactor of details of your Task. That way CloudReactor can start and schedule your Task, and setup your Task as a service. See CloudReactor python ECS QuickStart for an example.
However, it may not be possible or desired to change your deployment process. Instead, you may configure the Task to be auto-created.
Auto-created Tasks are created the first time your Task runs. This means there is no need to inform the API server of the Task details (during deployment) before it runs. Instead, each time the module runs, it informs the API server of the Task details at the same time as it requests the creation of a Task Execution. One disadvantage of auto-created Tasks is that they are not available in the CloudReactor dashboard until the first time they run.
When configuring a Task to be auto-created, you must specify the name or UUID of the Run Environment in CloudReactor that the Task is associated with. The Run Environment must be created ahead of time, either by the Cloudreactor AWS Setup Wizard, or manually in the CloudReactor dashboard.
You can also specify more Task properties, such as Alert Methods and
external links in the dashboard, by setting the environment variable
PROC_WRAPPER_AUTO_CREATE_TASK_PROPS set to a JSON-encoded object that has the
CloudReactor Task schema.
Execution Methods¶
CloudReactor currently supports three Execution Methods:
If a Task is running in AWS ECS, CloudReactor is able to run additional Task Executions, provided the details of running the Task is provided during deployment with the AWS ECS CloudReactor Deployer, or if the Task is configured to be auto-created, and this module is run. In the second case, this module uses the ECS Metadata endpoint to detect the ECS Task settings, and sends them to the API server. CloudReactor can also schedule Tasks or setup long-running services using Tasks, provided they are run in AWS ECS.
If a Task is running in AWS Lambda, CloudReactor is able to run additional Task Executions after the first run of the function.
However, a Task may use the Unknown execution method if it is not running in AWS ECS, AWS Lambda, or AWS CodeBuild. If that is the case, CloudReactor won’t be able to start the Task in the dashboard or as part of a Workflow, schedule the Task, or setup a service with the Task. But the advantage is that the Task code can be executed by any method available to you, such as bare metal servers, VM’s, or Kubernetes. All Tasks in CloudReactor, regardless of execution method, have their history kept and are monitored.
This module detects the execution method your Task is running with and sends that information to the API server, provided you configure your Task to be auto-created.
Passive Tasks¶
Passive Tasks are Tasks that CloudReactor does not manage. This means scheduling and service setup must be handled by other means (cron jobs, supervisord, etc). However, Tasks marked as services or that have a schedule will still be monitored by CloudReactor, which will send notifications if a service Task goes down or a Task does not run on schedule.
The module reports to the API server that auto-created Tasks are passive,
unless you specify the --force-task-passive commmand-line option or
set the environment variable PROC_WRAPPER_TASK_IS_PASSIVE to FALSE.
If a Task uses the Unknown Execution Method, it must be marked as passive,
because CloudReactor does not know how to manage it.
Pre-requisites¶
If you just want to use this module to retry processes, limit execution time, or fetch secrets, you can use offline mode, in which case no CloudReactor API key is required. But CloudReactor offers a free tier so we hope you sign up for a free account to enable monitoring and/or management.
If you want CloudReactor to be able to start your Tasks, you should use the Cloudreactor AWS Setup Wizard to configure your AWS environment to run Tasks in ECS Fargate. You can skip this step if running in passive mode is OK for you.
If you want to use CloudReactor to manage or just monitor your Tasks, you need to create a Run Environment and an API key in the CloudReactor dashboard. The API key can be scoped to the Run Environment if you wish. The key must have at least the Task access level, but for an auto-created Task, it must have at least the Developer access level.
Installation¶
Nuitka¶
Standalone executables built by
nuitka
for 64-bit Linux are available, located in bin/nuitka. These executables bundle
python so you don’t need to have python installed on your machine. They also
bundle all optional library dependencies so you can fetch secrets from AWS
Secrets Manager and extract them with jsonpath-ng, for example.
Compared to executables built by PyInstaller (see below), they start up faster, and most likely are more efficient at runtime.
RHEL or derivatives¶
To download and run the wrapper on a RHEL/Fedora/Amazon Linux 2 machine:
RUN wget -nv https://github.com/CloudReactor/cloudreactor-procwrapper/raw/5.1.0/bin/nuitka/al2/5.1.0/proc_wrapper.bin
ENTRYPOINT ["proc_wrapper.bin"]
Example Dockerfiles of known working environments are available for Amazon Linux 2 and Fedora.
Fedora 27 or later are supported.
Debian based systems¶
On a Debian based (including Ubuntu) machine:
RUN wget -nv https://github.com/CloudReactor/cloudreactor-procwrapper/raw/5.1.0/bin/nuitka/debian-amd64/5.1.0/proc_wrapper.bin
ENTRYPOINT ["proc_wrapper.bin"]
See the example Dockerfile for a known working Debian environment.
Debian 10 (Buster) or later are supported.
PyInstaller¶
Standalone executables built by PyInstaller for 64-bit Linux and Windows are available, located in bin/pyinstaller.
These executables bundle
python so you don’t need to have python installed on your machine. They also
bundle all optional library dependencies so you can fetch secrets from AWS
Secrets Manager and extract them with jsonpath-ng, for example. Compared to
executables built by nuitka, they start up slower but might be more reliable.
RHEL or derivatives¶
To download and run the wrapper on a RHEL/Fedora/Amazon Linux 2 machine:
RUN wget -nv https://github.com/CloudReactor/cloudreactor-procwrapper/raw/5.1.0/bin/pyinstaller/al2/5.1.0/proc_wrapper.bin
ENTRYPOINT ["proc_wrapper.bin"]
Example Dockerfiles of known working environments are available for Amazon Linux 2 and Fedora.
Fedora 27 or later are supported.
Debian based machines¶
On a Debian based (including Ubuntu) machine:
RUN wget -nv https://github.com/CloudReactor/cloudreactor-procwrapper/raw/5.1.0/bin/pyinstaller/debian-amd64/5.1.0/proc_wrapper.bin
ENTRYPOINT ["proc_wrapper.bin"]
See the example Dockerfile for a known working Debian environment.
Debian 10 (Buster) or later are supported.
Special thanks to wine and PyInstaller Docker Images for making it possible to cross-compile!
When python is available¶
Install this module via pip (or your favorite package manager):
pip install cloudreactor-procwrapper
Fetching secrets from AWS Secrets Manager requires that boto3 is available to import in your python environment.
JSON Path transformation requires that jsonpath-ng be available to import in your python environment.
You can get the tested versions of both dependencies in proc_wrapper-requirements.in (suitable for use by pip-tools) or the resolved requirements in proc_wrapper-requirements.txt.
Usage¶
There are two ways of using the module: wrapped mode and embedded mode.
Wrapped mode¶
In wrapped mode, you pass a command line to the module which it executes in a child process. The command can be implemented in whatever programming language the running machine supports.
Instead of running
somecommand --somearg x
you would run
./proc_wrapper somecommand --somearg x
assuming that are using the PyInstaller standalone executable, and that you configure the program using environment variables.
Or, if you have python installed:
python -m proc_wrapper somecommand --somearg x
Or, if using pipx, first install with:
pipx install cloudreactor_procwrapper
then run with
proc_wrapper somecommand --somearg x
Here are all the options:
usage: proc_wrapper [-h] [-v] [-n TASK_NAME] [--task-uuid TASK_UUID] [-a]
[--auto-create-task-run-environment-name AUTO_CREATE_TASK_RUN_ENVIRONMENT_NAME]
[--auto-create-task-run-environment-uuid AUTO_CREATE_TASK_RUN_ENVIRONMENT_UUID]
[--auto-create-task-props AUTO_CREATE_TASK_PROPS]
[--force-task-active]
[--task-execution-uuid TASK_EXECUTION_UUID]
[--task-version-number TASK_VERSION_NUMBER]
[--task-version-text TASK_VERSION_TEXT]
[--task-version-signature TASK_VERSION_SIGNATURE]
[--execution-method-props EXECUTION_METHOD_PROPS]
[--task-instance-metadata TASK_INSTANCE_METADATA] [-s]
[--schedule SCHEDULE] [--max-concurrency MAX_CONCURRENCY]
[--max-conflicting-age MAX_CONFLICTING_AGE]
[--api-base-url API_BASE_URL] [-k API_KEY]
[--api-heartbeat-interval API_HEARTBEAT_INTERVAL]
[--api-error-timeout API_ERROR_TIMEOUT]
[--api-final-update-timeout API_FINAL_UPDATE_TIMEOUT]
[--api-retry-delay API_RETRY_DELAY]
[--api-resume-delay API_RESUME_DELAY]
[--api-task-execution-creation-error-timeout API_TASK_EXECUTION_CREATION_ERROR_TIMEOUT]
[--api-task-execution-creation-conflict-timeout API_TASK_EXECUTION_CREATION_CONFLICT_TIMEOUT]
[--api-task-execution-creation-conflict-retry-delay API_TASK_EXECUTION_CREATION_CONFLICT_RETRY_DELAY]
[--api-request-timeout API_REQUEST_TIMEOUT] [-o] [-p]
[-d DEPLOYMENT] [--send-pid] [--send-hostname]
[--no-send-runtime-metadata]
[-l {DEBUG,INFO,WARNING,ERROR,CRITICAL}] [--log-secrets]
[--exclude-timestamps-in-log] [-w WORK_DIR]
[-c COMMAND_LINE] [--shell-mode {auto,enable,disable}]
[--no-strip-shell-wrapping]
[--no-process-group-termination] [-t PROCESS_TIMEOUT]
[-r PROCESS_MAX_RETRIES]
[--process-retry-delay PROCESS_RETRY_DELAY]
[--process-check-interval PROCESS_CHECK_INTERVAL]
[--process-termination-grace-period PROCESS_TERMINATION_GRACE_PERIOD]
[--enable-status-update-listener]
[--status-update-socket-port STATUS_UPDATE_SOCKET_PORT]
[--status-update-message-max-bytes STATUS_UPDATE_MESSAGE_MAX_BYTES]
[--status-update-interval STATUS_UPDATE_INTERVAL]
[-e ENV_LOCATIONS] [--config CONFIG_LOCATIONS]
[--config-merge-strategy {DEEP,SHALLOW,REPLACE,ADDITIVE,TYPESAFE_REPLACE,TYPESAFE_ADDITIVE}]
[--overwrite_env_during_resolution]
[--config-ttl CONFIG_TTL]
[--no-fail-fast-config-resolution]
[--resolved-env-var-name-prefix RESOLVED_ENV_VAR_NAME_PREFIX]
[--resolved-env-var-name-suffix RESOLVED_ENV_VAR_NAME_SUFFIX]
[--resolved-config-property-name-prefix RESOLVED_CONFIG_PROPERTY_NAME_PREFIX]
[--resolved-config-property-name-suffix RESOLVED_CONFIG_PROPERTY_NAME_SUFFIX]
[--env-var-name-for-config ENV_VAR_NAME_FOR_CONFIG]
[--config-property-name-for-env CONFIG_PROPERTY_NAME_FOR_ENV]
[--rollbar-access-token ROLLBAR_ACCESS_TOKEN]
[--rollbar-retries ROLLBAR_RETRIES]
[--rollbar-retry-delay ROLLBAR_RETRY_DELAY]
[--rollbar-timeout ROLLBAR_TIMEOUT]
...
Wraps the execution of processes so that a service API endpoint (CloudReactor)
is optionally informed of the progress. Also implements retries, timeouts, and
secret injection into the environment.
positional arguments:
command
optional arguments:
-h, --help show this help message and exit
-v, --version Print the version and exit
task:
Task settings
-n TASK_NAME, --task-name TASK_NAME
Name of Task (either the Task Name or the Task UUID
must be specified
--task-uuid TASK_UUID
UUID of Task (either the Task Name or the Task UUID
must be specified)
-a, --auto-create-task
Create the Task even if not known by the API server
--auto-create-task-run-environment-name AUTO_CREATE_TASK_RUN_ENVIRONMENT_NAME
Name of the Run Environment to use if auto-creating
the Task (either the name or UUID of the Run
Environment must be specified if auto-creating the
Task). Defaults to the deployment name if the Run
Environment UUID is not specified.
--auto-create-task-run-environment-uuid AUTO_CREATE_TASK_RUN_ENVIRONMENT_UUID
UUID of the Run Environment to use if auto-creating
the Task (either the name or UUID of the Run
Environment must be specified if auto-creating the
Task)
--auto-create-task-props AUTO_CREATE_TASK_PROPS
Additional properties of the auto-created Task, in
JSON format. See https://apidocs.cloudreactor.io/#oper
ation/api_v1_tasks_create for the schema.
--force-task-active Indicates that the auto-created Task should be
scheduled and made a service by the API server, if
applicable. Otherwise, auto-created Tasks are marked
passive.
--task-execution-uuid TASK_EXECUTION_UUID
UUID of Task Execution to attach to
--task-version-number TASK_VERSION_NUMBER
Numeric version of the Task's source code
--task-version-text TASK_VERSION_TEXT
Human readable version of the Task's source code
--task-version-signature TASK_VERSION_SIGNATURE
Version signature of the Task's source code (such as a
git commit hash)
--execution-method-props EXECUTION_METHOD_PROPS
Additional properties of the execution method, in JSON
format. See https://apidocs.cloudreactor.io/#operation
/api_v1_task_executions_create for the schema.
--task-instance-metadata TASK_INSTANCE_METADATA
Additional metadata about the Task instance, in JSON
format
-s, --service Indicate that this is a Task that should run
indefinitely
--schedule SCHEDULE Run schedule reported to the API server
--max-concurrency MAX_CONCURRENCY
Maximum number of concurrent Task Executions of the
same Task. Defaults to 1.
--max-conflicting-age MAX_CONFLICTING_AGE
Maximum age of conflicting Tasks to consider, in
seconds. -1 means no limit. Defaults to the heartbeat
interval, plus 60 seconds for services that send
heartbeats. Otherwise, defaults to no limit.
api:
API client settings
--api-base-url API_BASE_URL
Base URL of API server. Defaults to
https://api.cloudreactor.io
-k API_KEY, --api-key API_KEY
API key. Must have at least the Task access level, or
Developer access level for auto-created Tasks.
--api-heartbeat-interval API_HEARTBEAT_INTERVAL
Number of seconds to wait between sending heartbeats
to the API server. -1 means to not send heartbeats.
Defaults to 30 for concurrency limited services, 300
otherwise.
--api-error-timeout API_ERROR_TIMEOUT
Number of seconds to wait while receiving recoverable
errors from the API server. Defaults to 300.
--api-final-update-timeout API_FINAL_UPDATE_TIMEOUT
Number of seconds to wait while receiving recoverable
errors from the API server when sending the final
update before exiting. Defaults to 1800.
--api-retry-delay API_RETRY_DELAY
Number of seconds to wait before retrying an API
request. Defaults to 120.
--api-resume-delay API_RESUME_DELAY
Number of seconds to wait before resuming API
requests, after retries are exhausted. Defaults to
600. -1 means to never resume.
--api-task-execution-creation-error-timeout API_TASK_EXECUTION_CREATION_ERROR_TIMEOUT
Number of seconds to keep retrying Task Execution
creation while receiving error responses from the API
server. -1 means to keep trying indefinitely. Defaults
to 300.
--api-task-execution-creation-conflict-timeout API_TASK_EXECUTION_CREATION_CONFLICT_TIMEOUT
Number of seconds to keep retrying Task Execution
creation while conflict is detected by the API server.
-1 means to keep trying indefinitely. Defaults to 1800
for concurrency limited services, 0 otherwise.
--api-task-execution-creation-conflict-retry-delay API_TASK_EXECUTION_CREATION_CONFLICT_RETRY_DELAY
Number of seconds between attempts to retry Task
Execution creation after conflict is detected.
Defaults to 60 for concurrency-limited services, 120
otherwise.
--api-request-timeout API_REQUEST_TIMEOUT
Timeout for contacting API server, in seconds.
Defaults to 30.
-o, --offline-mode Do not communicate with or rely on an API server
-p, --prevent-offline-execution
Do not start processes if the API server is
unavailable or the wrapper is misconfigured.
-d DEPLOYMENT, --deployment DEPLOYMENT
Deployment name (production, staging, etc.)
--send-pid Send the process ID to the API server
--send-hostname Send the hostname to the API server
--no-send-runtime-metadata
Do not send metadata about the runtime environment
log:
Logging settings
-l {DEBUG,INFO,WARNING,ERROR,CRITICAL}, --log-level {DEBUG,INFO,WARNING,ERROR,CRITICAL}
Log level
--log-secrets Log sensitive information
--exclude-timestamps-in-log
Exclude timestamps in log (possibly because the log
stream will be enriched by timestamps automatically by
a logging service like AWS CloudWatch Logs)
process:
Process settings
-w WORK_DIR, --work-dir WORK_DIR
Working directory. Defaults to the current directory.
-c COMMAND_LINE, --command-line COMMAND_LINE
Command line to execute
--shell-mode {auto,enable,disable}
Indicates if the process command should be executed in
a shell. Executing in a shell allows shell scripts,
commands, and expressions to be used, with the
disadvantage that termination signals may not be
propagated to child processes. Options are: enable --
Force the command to be executed in a shell; disable
-- Force the command to be executed without a shell;
auto -- Auto-detect the shell mode by analyzing the
command.
--no-strip-shell-wrapping
Do not strip the command-line of shell wrapping like
"/bin/sh -c" that can be introduced by Docker when
using shell form of ENTRYPOINT and CMD.
--no-process-group-termination
Send termination and kill signals to the wrapped
process only, instead of its process group (which is
the default). Sending to the process group allows all
child processes to receive the signals, even if the
wrapped process does not forward signals. However, if
your wrapped process manually handles and forwards
signals to its child processes, you probably want to
send signals to only your wrapped process.
-t PROCESS_TIMEOUT, --process-timeout PROCESS_TIMEOUT
Timeout for process completion, in seconds. -1 means
no timeout, which is the default.
-r PROCESS_MAX_RETRIES, --process-max-retries PROCESS_MAX_RETRIES
Maximum number of times to retry failed processes. -1
means to retry forever. Defaults to 0.
--process-retry-delay PROCESS_RETRY_DELAY
Number of seconds to wait before retrying a process.
Defaults to 60.
--process-check-interval PROCESS_CHECK_INTERVAL
Number of seconds to wait between checking the status
of processes. Defaults to 10.
--process-termination-grace-period PROCESS_TERMINATION_GRACE_PERIOD
Number of seconds to wait after sending SIGTERM to a
process, but before killing it with SIGKILL. Defaults
to 30.
updates:
Status update settings
--enable-status-update-listener
Listen for status updates from the process, sent on
the status socket port via UDP. If not specified,
status update messages will not be read.
--status-update-socket-port STATUS_UPDATE_SOCKET_PORT
The port used to receive status updates from the
process. Defaults to 2373.
--status-update-message-max-bytes STATUS_UPDATE_MESSAGE_MAX_BYTES
The maximum number of bytes status update messages can
be. Defaults to 65536.
--status-update-interval STATUS_UPDATE_INTERVAL
Minimum of number of seconds to wait between sending
status updates to the API server. -1 means to not send
status updates except with heartbeats. Defaults to -1.
configuration:
Environment/configuration resolution settings
-e ENV_LOCATIONS, --env ENV_LOCATIONS
Location of either local file, AWS S3 ARN, or AWS
Secrets Manager ARN containing properties used to
populate the environment for embedded mode, or the
process environment for wrapped mode. By default, the
file format is assumed to be dotenv. Specify multiple
times to include multiple locations.
--config CONFIG_LOCATIONS
Location of either local file, AWS S3 ARN, or AWS
Secrets Manager ARN containing properties used to
populate the configuration for embedded mode. By
default, the file format is assumed to be in JSON.
Specify multiple times to include multiple locations.
--config-merge-strategy {DEEP,SHALLOW,REPLACE,ADDITIVE,TYPESAFE_REPLACE,TYPESAFE_ADDITIVE}
Merge strategy for merging configurations. Defaults to
'DEEP', which does not require mergedeep. Besides the
'SHALLOW' strategy, all other strategies require the
mergedeep python package to be installed.
--overwrite_env_during_resolution
Overwrite existing environment variables when
resolving them
--config-ttl CONFIG_TTL
Number of seconds to cache resolved environment
variables and configuration properties instead of
refreshing them when a process restarts. -1 means to
never refresh. Defaults to -1.
--no-fail-fast-config-resolution
Exit immediately if an error occurs resolving the
configuration
--resolved-env-var-name-prefix RESOLVED_ENV_VAR_NAME_PREFIX
Required prefix for names of environment variables
that should resolved. The prefix will be removed in
the resolved variable name. Defaults to ''.
--resolved-env-var-name-suffix RESOLVED_ENV_VAR_NAME_SUFFIX
Required suffix for names of environment variables
that should resolved. The suffix will be removed in
the resolved variable name. Defaults to
'_FOR_PROC_WRAPPER_TO_RESOLVE'.
--resolved-config-property-name-prefix RESOLVED_CONFIG_PROPERTY_NAME_PREFIX
Required prefix for names of configuration properties
that should resolved. The prefix will be removed in
the resolved property name. Defaults to ''.
--resolved-config-property-name-suffix RESOLVED_CONFIG_PROPERTY_NAME_SUFFIX
Required suffix for names of configuration properties
that should resolved. The suffix will be removed in
the resolved property name. Defaults to
'__to_resolve'.
--env-var-name-for-config ENV_VAR_NAME_FOR_CONFIG
The name of the environment variable used to set to
the value of the JSON encoded configuration. Defaults
to not setting any environment variable.
--config-property-name-for-env CONFIG_PROPERTY_NAME_FOR_ENV
The name of the configuration property used to set to
the value of the JSON encoded environment. Defaults to
not setting any property.
rollbar:
Rollbar settings
--rollbar-access-token ROLLBAR_ACCESS_TOKEN
Access token for Rollbar (used to report error when
communicating with API server)
--rollbar-retries ROLLBAR_RETRIES
Number of retries per Rollbar request. Defaults to 2.
--rollbar-retry-delay ROLLBAR_RETRY_DELAY
Number of seconds to wait before retrying a Rollbar
request. Defaults to 120.
--rollbar-timeout ROLLBAR_TIMEOUT
Timeout for contacting Rollbar server, in seconds.
Defaults to 30.
These environment variables take precedence over command-line arguments:
PROC_WRAPPER_TASK_NAME
PROC_WRAPPER_TASK_UUID
PROC_WRAPPER_TASK_EXECUTION_UUID
PROC_WRAPPER_AUTO_CREATE_TASK (TRUE or FALSE)
PROC_WRAPPER_AUTO_CREATE_TASK_RUN_ENVIRONMENT_NAME
PROC_WRAPPER_AUTO_CREATE_TASK_RUN_ENVIRONMENT_UUID
PROC_WRAPPER_AUTO_CREATE_TASK_PROPS (JSON encoded property map)
PROC_WRAPPER_TASK_IS_PASSIVE (TRUE OR FALSE)
PROC_WRAPPER_TASK_IS_SERVICE (TRUE or FALSE)
PROC_WRAPPER_EXECUTION_METHOD_PROPS (JSON encoded property map)
PROC_WRAPPER_TASK_MAX_CONCURRENCY (set to -1 to indicate no limit)
PROC_WRAPPER_PREVENT_OFFLINE_EXECUTION (TRUE or FALSE)
PROC_WRAPPER_TASK_VERSION_NUMBER
PROC_WRAPPER_TASK_VERSION_TEXT
PROC_WRAPPER_TASK_VERSION_SIGNATURE
PROC_WRAPPER_TASK_INSTANCE_METADATA (JSON encoded property map)
PROC_WRAPPER_LOG_LEVEL (TRACE, DEBUG, INFO, WARNING, ERROR, or CRITICAL)
PROC_WRAPPER_LOG_SECRETS (TRUE or FALSE)
PROC_WRAPPER_INCLUDE_TIMESTAMPS_IN_LOG (TRUE or FALSE)
PROC_WRAPPER_DEPLOYMENT
PROC_WRAPPER_API_BASE_URL
PROC_WRAPPER_API_KEY
PROC_WRAPPER_API_HEARTBEAT_INTERVAL_SECONDS
PROC_WRAPPER_API_ERROR_TIMEOUT_SECONDS
PROC_WRAPPER_API_RETRY_DELAY_SECONDS
PROC_WRAPPER_API_RESUME_DELAY_SECONDS
PROC_WRAPPER_API_TASK_EXECUTION_CREATION_ERROR_TIMEOUT_SECONDS
PROC_WRAPPER_API_TASK_EXECUTION_CREATION_CONFLICT_TIMEOUT_SECONDS
PROC_WRAPPER_API_TASK_EXECUTION_CREATION_CONFLICT_RETRY_DELAY_SECONDS
PROC_WRAPPER_API_FINAL_UPDATE_TIMEOUT_SECONDS
PROC_WRAPPER_API_REQUEST_TIMEOUT_SECONDS
PROC_WRAPPER_ENV_LOCATIONS (comma-separated list of locations)
PROC_WRAPPER_CONFIG_LOCATIONS (comma-separated list of locations)
PROC_WRAPPER_OVERWRITE_ENV_WITH_SECRETS (TRUE or FALSE)
PROC_WRAPPER_RESOLVE_SECRETS (TRUE or FALSE)
PROC_WRAPPER_MAX_CONFIG_RESOLUTION_DEPTH
PROC_WRAPPER_MAX_CONFIG_RESOLUTION_ITERATIONS
PROC_WRAPPER_CONFIG_TTL_SECONDS
PROC_WRAPPER_FAIL_FAST_CONFIG_RESOLUTION (TRUE or FALSE)
PROC_WRAPPER_RESOLVABLE_ENV_VAR_NAME_PREFIX
PROC_WRAPPER_RESOLVABLE_ENV_VAR_NAME_SUFFIX
PROC_WRAPPER_RESOLVABLE_CONFIG_PROPERTY_NAME_PREFIX
PROC_WRAPPER_RESOLVABLE_CONFIG_PROPERTY_NAME_SUFFIX
PROC_WRAPPER_ENV_VAR_NAME_FOR_CONFIG
PROC_WRAPPER_CONFIG_PROPERTY_NAME_FOR_ENV
PROC_WRAPPER_SEND_PID (TRUE or FALSE)
PROC_WRAPPER_SEND_HOSTNAME (TRUE or FALSE)
PROC_WRAPPER_SEND_RUNTIME_METADATA (TRUE or FALSE)
PROC_WRAPPER_ROLLBAR_ACCESS_TOKEN
PROC_WRAPPER_ROLLBAR_TIMEOUT_SECONDS
PROC_WRAPPER_ROLLBAR_RETRIES
PROC_WRAPPER_ROLLBAR_RETRY_DELAY_SECONDS
PROC_WRAPPER_MAX_CONFLICTING_AGE_SECONDS
PROC_WRAPPER_TASK_COMMAND
PROC_WRAPPER_SHELL_MODE (TRUE or FALSE)
PROC_WRAPPER_STRIP_SHELL_WRAPPING (TRUE or FALSE)
PROC_WRAPPER_WORK_DIR
PROC_WRAPPER_PROCESS_MAX_RETRIES
PROC_WRAPPER_PROCESS_TIMEOUT_SECONDS
PROC_WRAPPER_PROCESS_RETRY_DELAY_SECONDS
PROC_WRAPPER_PROCESS_CHECK_INTERVAL_SECONDS
PROC_WRAPPER_PROCESS_TERMINATION_GRACE_PERIOD_SECONDS
PROC_WRAPPER_PROCESS_GROUP_TERMINATION (TRUE or FALSE)
PROC_WRAPPER_STATUS_UPDATE_SOCKET_PORT
PROC_WRAPPER_STATUS_UPDATE_MESSAGE_MAX_BYTES
With the exception of the settings for Secret Fetching and Resolution, these environment variables are read after Secret Fetching so that they can come from secret values.
The command is executed with the same environment that the wrapper script gets, except that these properties are copied/overridden:
PROC_WRAPPER_DEPLOYMENT
PROC_WRAPPER_API_BASE_URL
PROC_WRAPPER_API_KEY
PROC_WRAPPER_API_ERROR_TIMEOUT_SECONDS
PROC_WRAPPER_API_RETRY_DELAY_SECONDS
PROC_WRAPPER_API_RESUME_DELAY_SECONDS
PROC_WRAPPER_API_REQUEST_TIMEOUT_SECONDS
PROC_WRAPPER_ROLLBAR_ACCESS_TOKEN
PROC_WRAPPER_ROLLBAR_TIMEOUT_SECONDS
PROC_WRAPPER_ROLLBAR_RETRIES
PROC_WRAPPER_ROLLBAR_RETRY_DELAY_SECONDS
PROC_WRAPPER_ROLLBAR_RESUME_DELAY_SECONDS
PROC_WRAPPER_TASK_EXECUTION_UUID
PROC_WRAPPER_TASK_UUID
PROC_WRAPPER_TASK_NAME
PROC_WRAPPER_TASK_VERSION_NUMBER
PROC_WRAPPER_TASK_VERSION_TEXT
PROC_WRAPPER_TASK_VERSION_SIGNATURE
PROC_WRAPPER_TASK_INSTANCE_METADATA
PROC_WRAPPER_SCHEDULE
PROC_WRAPPER_PROCESS_TIMEOUT_SECONDS
PROC_WRAPPER_TASK_MAX_CONCURRENCY
PROC_WRAPPER_PREVENT_OFFLINE_EXECUTION
PROC_WRAPPER_PROCESS_TERMINATION_GRACE_PERIOD_SECONDS
PROC_WRAPPER_ENABLE_STATUS_UPDATE_LISTENER
PROC_WRAPPER_STATUS_UPDATE_SOCKET_PORT
PROC_WRAPPER_STATUS_UPDATE_INTERVAL_SECONDS
PROC_WRAPPER_STATUS_UPDATE_MESSAGE_MAX_BYTES
Wrapped mode is suitable for running in a shell on your own (virtual) machine or in a Docker container. It requires multi-process support, as the module runs at the same time as the command it wraps.
Embedded mode¶
You can use embedded mode to execute python code from inside a python program.
Include the proc_wrapper package in your python project’s
dependencies. To run a task you want to be monitored:
from typing import Any, Mapping
from proc_wrapper import ProcWrapper, ProcWrapperParams
def fun(wrapper: ProcWrapper, cbdata: dict[str, int],
config: Mapping[str, Any]) -> int:
print(cbdata)
return cbdata['a']
# This is the function signature of a function invoked by AWS Lambda.
def entrypoint(event: Any, context: Any) -> int:
params = ProcWrapperParams()
params.auto_create_task = True
# If the Task Execution is running in AWS Lambda, CloudReactor can make
# the associated Task available to run (non-passive) in the CloudReactor
# dashboard or by API, after the wrapper reports its first execution.
proc_wrapper_params.task_is_passive = False
params.task_name = 'embedded_test_production'
params.auto_create_task_run_environment_name = 'production'
# For example only, in the real world you would use Secret Fetching;
# see below.
params.api_key = 'YOUR_CLOUDREACTOR_API_KEY'
# In an AWS Lambda environment, passing the context and event allows
# CloudReactor to monitor and manage this Task.
proc_wrapper = ProcWrapper(params=params, runtime_context=context,
input_value=event)
x = proc_wrapper.managed_call(fun, {'a': 1, 'b': 2})
# Should print 1
print(x)
return x
This is suitable for running in single-threaded environments like AWS Lambda, or as part of a larger process that executes sub-routines that should be monitored. See cloudreactor-python-lambda-quickstart for an example project that uses proc_wrapper in a function run by AWS Lambda.
Embedded mode configuration¶
In embedded mode, besides setting properties of ProcWrapperParams in code,
ProcWrapper can be also configured in two ways:
First, using environment variables, as in wrapped mode.
Second, using the configuration dictionary. If the configuration dictionary
contains the key proc_wrapper_params and its value is a dictionary, the
keys and values in the dictionary will be used to to set these attributes in
ProcWrapperParams:
Key |
Type |
Mutable |
Uses Resolved Config |
|---|---|---|---|
log_secrets |
bool |
No |
No |
env_locations |
list[str] |
No |
No |
config_locations |
list[str] |
No |
No |
config_merge_strategy |
str |
No |
No |
overwrite_env_during_resolution |
bool |
No |
No |
max_config_resolution_depth |
int |
No |
No |
max_config_resolution_iterations |
int |
No |
No |
config_ttl |
int |
No |
No |
fail_fast_config_resolution |
bool |
No |
No |
resolved_env_var_name_prefix |
str |
No |
No |
resolved_env_var_name_suffix |
str |
No |
No |
resolved_config_property_name_prefix |
str |
No |
No |
resolved_config_property_name_suffix |
str |
No |
No |
schedule |
str |
No |
Yes |
max_concurrency |
int |
No |
Yes |
max_conflicting_age |
int |
No |
Yes |
offline_mode |
bool |
No |
Yes |
prevent_offline_execution |
bool |
No |
Yes |
service |
bool |
No |
Yes |
deployment |
str |
No |
Yes |
api_base_url |
str |
No |
Yes |
api_heartbeat_interval |
int |
No |
Yes |
enable_status_listener |
bool |
No |
Yes |
status_update_socket_port |
int |
No |
Yes |
status_update_message_max_bytes |
int |
No |
Yes |
status_update_interval |
int |
No |
Yes |
log_level |
str |
No |
Yes |
include_timestamps_in_log |
bool |
No |
Yes |
api_key |
str |
Yes |
Yes |
api_request_timeout |
int |
Yes |
Yes |
api_error_timeout |
int |
Yes |
Yes |
api_retry_delay |
int |
Yes |
Yes |
api_resume_delay |
int |
Yes |
Yes |
api_task_execution_creation_error_timeout |
int |
Yes |
Yes |
api_task_execution_creation_conflict_timeout |
int |
Yes |
Yes |
api_task_execution_creation_conflict_retry_delay |
int |
Yes |
Yes |
process_timeout |
int |
Yes |
Yes |
process_max_retries |
int |
Yes |
Yes |
process_retry_delay |
int |
Yes |
Yes |
command |
list[str] |
Yes |
Yes |
command_line |
str |
Yes |
Yes |
shell_mode |
bool |
Yes |
Yes |
strip_shell_wrapping |
bool |
Yes |
Yes |
work_dir |
str |
Yes |
Yes |
process_termination_grace_period |
int |
Yes |
Yes |
send_pid |
bool |
Yes |
Yes |
send_hostname |
bool |
Yes |
Yes |
send_runtime_metadata |
bool |
Yes |
Yes |
Keys that are marked with “Mutable” – “No” in the table above can be
overridden when the configuration is reloaded after the config_ttl expires.
Keys that are marked as “Uses Resolved Config” – “Yes” in the table above can come from the resolved configuration after secret resolution (see below).
Secret Fetching and Resolution¶
A common requirement is that deployed code / images do not contain secrets internally which could be decompiled. Instead, programs should fetch secrets from an external source in a secure manner. If your program runs in AWS, it can make use of AWS’s roles that have permission to access data in Secrets Manager or S3. However, in many scenarios, having your program access AWS directly has the following disadvantages:
Your program becomes coupled to AWS, so it is difficult to run locally or switch to another infrastructure provider
You need to write code or use a library for each programming language you use, so secret fetching is done in a non-uniform way
Writing code to merge and parse secrets from different sources is tedious
Therefore, proc_wrapper implements Secret Fetching and Resolution to solve these problems so your programs don’t have to. Both usage modes can fetch secrets from AWS Secrets Manager, AWS S3, or the local filesystem, and optionally extract embedded data into the environment or a configuration dictionary. The environment is used to pass values to processes run in wrapped mode, while the configuration dictionary is passed to the callback function in embedded mode.
proc_wrapper parses secret location strings that specify the how to resolve a secret value. Each secret location string has the format:
[PROVIDER_CODE:]<Provider specific address>[!FORMAT][|JP:<JSON Path expression>]
Secret Providers¶
Providers indicate the raw source of the secret data. The table below lists the supported providers:
Provider Code |
Value Prefix |
Provider |
Example Address |
Required libs |
Notes |
|---|---|---|---|---|---|
|
|
AWS Secrets Manager |
|
Can also include version suffix like |
|
|
|
AWS S3 Object |
|
||
|
|
Local file |
|
The default provider if no provider is auto-detected |
|
|
The process environment |
|
The name of another environment variable |
||
|
The configuration dictionary |
|
JSON path expression to extract the data in the configuration |
||
|
Plaintext |
|
If you don’t specify an explicit provider prefix in a secret location
(e.g. AWS_SM:), the provider can be auto-detected from the address portion
using the Value Prefix. For example the secret location
arn:aws:s3:::examplebucket/staging/app1/config.json will be auto-detected
to with the AWS_S3 provider because it starts with arn:aws:s3:::.
Secret Formats¶
Formats indicate how the raw string data is parsed into a secret value (which may be a string, number, boolean, dictionary, or array). The table below lists the supported formats:
Format Code |
Extensions |
MIME types |
Required libs |
Notes |
|---|---|---|---|---|
|
|
None |
Also auto-detected if location includes |
|
|
|
|
||
|
|
|
|
The format of a secret value can be auto-detected from the extension or by the
MIME type if available. Otherwise, you may need to an explicit format code
(e.g. !yaml).
AWS Secrets Manager / S3 notes¶
boto3
is used to fetch secrets. It will try to access to AWS Secrets Manager
or S3 using environment variables AWS_ACCESS_KEY_ID and
AWS_SECRET_ACCESS_KEY if they are set, or use the EC2 instance role, ECS task
role, or Lambda execution role if available.
For Secrets Manager, you can also use “partial ARNs” (without the hyphened suffix) as keys. In the example above
arn:aws:secretsmanager:us-east-2:1234567890:secret:config
could be used to fetch the same secret, provided there are no conflicting secret ARNs. This allows you to get the latest version of the secret.
If the secret was stored in Secrets Manager as binary, the corresponding value will be set to the Base-64 encoded value.
If you’re deploying a python function using AWS Lambda, note that boto3 is already included in the available packages, so there’s no need to include it (unless the bundled version isn’t compatible). Also we strongly encourage you to add:
logging.getLogger("botocore").setLevel(logging.INFO)
to your code if you are using proc_wrapper for secrets resolution. This prevent secrets from Secrets Manager from being leaked. For details, see this issue.
Secret Tranformation¶
Fetching secrets can be relatively expensive and it makes sense to group related secrets together. Therefore it is common to store dictionaries (formatted as JSON or YAML) as secrets. However, each desired environment variable or configuration property may only consist of a fragment of the dictionary. For example, given the JSON-formatted dictionary
{
"username": "postgres",
"password": "badpassword"
}
you may want to populate the environment variable DB_USERNAME with
postgres.
To facilitate this, dictionary fragments can be extracted to individual
environment variables using jsonpath-ng.
To specify that a variable be extracted from a dictionary using
a JSON Path expression, append |JP: followed by the JSON Path expression
to the secret location string. For example, if the AWS Secrets Manager
ARN
arn:aws:secretsmanager:us-east-2:1234567890:secret:db-PPrpY
contains the dictionary above, then the secret location string
arn:aws:secretsmanager:us-east-2:1234567890:secret:db-PPrpY|JP:$.username
will resolve to postgres as desired.
If you do something similar to get the password from the same JSON value, proc_wrapper is smart enough to cache the fetched dictionary, so that the raw data is only fetched once.
Since JSON path expressions yield a list of results, the secrets fetcher implements the following rules to transform the list to the final value:
If the list of results has a single value, that value is used as the final value, unless
[*]is appended to the JSON path expression.Otherwise, the final value is the list of results
Fetching from another environment variable¶
In some deployment scenarios, multiple secrets can be injected into a single environment variable as a JSON encoded object. In that case, the module can extract secrets using the ENV secret source. For example, you may have arranged to have the environment variable DB_CONFIG injected with the JSON encoded value:
{ "username": "postgres", "password": "nohackme" }
Then to extract the username to the environment variable DB_USERNAME you you would add the environment variable DB_USER_FOR_PROC_WRAPPER_TO_RESOLVE set to
ENV:DB_CONFIG|JP:$.username
Secret injection into environment and configuration¶
Now let’s use secret location strings to inject the values into the environment (for wrapped mode) and/or the the configuration dictionary (for embedded mode). proc_wrapper supports two methods of secret injection which can be combined together:
Top-level fetching
Secrets Resolution
Top-level fetching¶
Top-level fetching refers to fetching a dictionary that contains multiple secrets and populating the environment / configuration dictionary with it. To use top-level fetching, you specify the secret locations from which you want to fetch the secrets and the corresponding values are merged together into the environment / configuration.
To use top-level fetching in wrapped mode, populate the
environment variables PROC_WRAPPER_ENV_LOCATIONS with a comma-separated
list of secret locations, or use the command-line option
--env-locations <secret_location> one or more times. Secret location
strings passed in via PROC_WRAPPER_ENV_LOCATIONS or --env-locations
will be parsed as dotenv files unless format is auto-detected or
explicitly specified.
To use top-level fetching in embedded mode, set the ProcWrapperParams property
config_locations to a list of secret locations. Alternatively, you can set
the environment variable PROC_WRAPPER_CONFIG_LOCATIONS to a comma-separated
list, and this will be picked up automatically. Secret location values
will be parsed as JSON unless the format is auto-detected or explicitly
specified. The config argument
passed to the your callback function will contain a merged dictionary of all
fetched and parsed dictionary values. For example:
def callback(wrapper: ProcWrapper, cbdata: str,
config: Dict[str, Any]) -> str:
return "super" + cbdata + config["username"]
def main():
params = ProcWrapperParams()
# Optional: you can set an initial configuration dictionary which will
# have its values included in the final configuration unless overridden.
params.initial_config = {
"log_level": "DEBUG"
}
# You can omit this if you set PROC_WRAPPER_CONFIG_LOCATIONS environment
# variable to the same ARN
params.config_locations = [
"arn:aws:secretsmanager:us-east-2:1234567890:secret:db-PPrpY",
# More secret locations can be added here, and their values will
# be merged
]
wrapper = ProcWrapper(params=params)
# Returns "superduperpostgres"
return wrapper.managed_call(callback, "duper")
Merging Secrets¶
Top-level fetching can potentially fetch multiple dictionaries which are
merged together in the final environment / configuration dictionary.
The default merge strategy (DEEP) merges recursively, even dictionaries
in lists. The SHALLOW merge strategy just overwrites top-level keys, with later
secret locations taking precedence. However, if you include the
mergedeep library, you can also
set the merge strategy to one of:
REPLACEADDITIVETYPESAFE_REPLACETYPESAFE_ADDITIVE
so that nested lists can be appended to instead of replaced (in the case of the
ADDITIVE strategies), or errors will be raised if incompatibly-typed values
are merged (in the case of the TYPESAFE strategies). In wrapped mode,
the merge strategy can be set with the --config-merge-strategy command-line
argument or PROC_WRAPPER_CONFIG_MERGE_STRATEGY environment variable. In
embedded mode, the merge strategy can be set in the
config_merge_strategy string property of ProcWrapperParams.
Secret Resolution¶
Secret Resolution substitutes configuration or environment values that are secret location strings with the computed values of those strings. Compared to Secret Fetching, Secret Resolution is more useful when you want more control over the names of variables or when you have secret values deep inside your configuration.
In wrapped mode, if you want to set the environment variable MY_SECRET with
a value fetched
from AWS Secrets Manager, you would set the environment variable
MY_SECRET_FOR_PROC_WRAPPER_TO_RESOLVE to a secret location string
which is ARN of the secret, for example:
arn:aws:secretsmanager:us-east-2:1234567890:secret:db-PPrpY
(The _FOR_PROC_WRAPPER_TO_RESOLVE suffix of environment variable names is
removed during resolution. It can also be configured with the PROC_WRAPPER_RESOLVABLE_ENV_VAR_NAME_SUFFIX environment variable.)
In embedded mode, if you want the final configuration dictionary to look like:
{
"db_username": "postgres",
"db_password": "badpassword",
...
}
The initial configuration dictionary would look like:
{
"db_username__to_resolve": "arn:aws:secretsmanager:us-east-2:1234567890:secret:db-PPrpY|JP:$.username",
"db_password__to_resolve": "arn:aws:secretsmanager:us-east-2:1234567890:secret:db-PPrpY|JP:$.password",
...
}
(The __to_resolve suffix (with 2 underscores!) of keys is removed during
resolution. It can also be configured with the resolved_config_property_name_suffix
property of ProcWrapperParams.)
proc_wrapper can also resolve keys in embedded dictionaries, like:
{
"db": {
"username__to_resolve": "arn:aws:secretsmanager:us-east-2:1234567890:secret:config-PPrpY|JP:$.username",
"password__to_resolve":
"arn:aws:secretsmanager:us-east-2:1234567890:secret:config-PPrpY|JP:$.password",
...
},
...
}
up to a maximum depth that you can control with ProcWrapperParams.max_config_resolution_depth (which defaults to 5). That would resolve to
{
"db": {
"username": "postgres",
"password": "badpassword"
...
},
...
}
You can also inject entire dictionaries, like:
{
"db__to_resolve": "arn:aws:secretsmanager:us-east-2:1234567890:secret:config-PPrpY",
...
}
which would resolve to
{
"db": {
"username": "postgres",
"password": "badpassword"
},
...
}
To enable secret resolution in wrapped mode, set environment variable PROC_WRAPPER_RESOLVE_SECRETS to TRUE. In embedded mode, secret
resolution is enabled by default; set the
max_config_resolution_iterations property of ProcWrapperParams to 0
to disable resolution.
Secret resolution is run multiple times so that if a resolved value contains
a secret location string, it will be resolved on the next pass. By default,
proc_wrapper limits the maximum number of resolution passes to 3 but you
can control this with the environment variable
PROC_WRAPPER_MAX_CONFIG_RESOLUTION_ITERATIONS in embedded mode,
or by setting the max_config_resolution_iterations property of
ProcWrapperParams in wrapped mode.
Environment Projection¶
During secret fetching and secret resolution, proc_wrapper internally maintains the computed environment as a dictionary which may have embedded lists and dictionaries. However, the final environment passed to the process is a flat dictionary containing only string values. So proc_wrapper converts all top-level values to strings using these rules:
Lists and dictionaries are converted to their JSON-encoded string value
Boolean values are converted to their upper-cased string representation (e.g. the string
FALSEfor the boolean valuefalse)The
Nonevalue is converted to the empty stringAll other values are converted using python’s
str()function
Secrets Refreshing¶
You can set a Time to Live (TTL) on the duration that secret values are cached. Caching helps reduce expensive lookups of secrets and bandwidth usage.
In wrapped mode, set the TTL of environment variables set from secret locations
using the --config-ttl command-line argument or
PROC_WRAPPER_CONFIG_TTL_SECONDS environment variable.
If the process exits, you have configured the script to retry,
and the TTL has expired since the last fetch,
proc_wrapper will re-fetch the secrets
and resolve them again, for the environment passed to the next invocation of
your process.
In embedded mode, set the TTL of configuration dictionary values set from
secret locations by setting the config_ttl property of
ProcWrapperParams. If 1) your callback function raises an exception, 2) you have
configured the script to retry; and 3) the TTL has expired since the last fetch,
proc_wrapper will re-fetch the secrets
and resolve them again, for the configuration passed to the next invocation of
the callback function.
Status Updates¶
Status Updates in Wrapped Mode¶
While your process in running, you can send status updates to CloudReactor by using the StatusUpdater class. Status updates are shown in the CloudReactor dashboard and allow you to track the current progress of a Task and also how many items are being processed in multiple executions over time.
In wrapped mode, your application code would send updates to the proc_wrapper program via UDP port 2373 (configurable with the PROC_WRAPPER_STATUS_UPDATE_PORT environment variable). If your application code is in python, you can use the provided StatusUpdater class to do this:
from proc_wrapper import StatusUpdater
with StatusUpdater() as updater:
updater.send_update(last_status_message="Starting ...")
success_count = 0
for i in range(100):
try:
do_work()
success_count += 1
updater.send_update(success_count=success_count)
except Exception:
failed_count += 1
updater.send_update(failed_count=failed_count)
updater.send_update(last_status_message="Finished!")
Status Updates in Embedded Mode¶
In embedded mode, your callback in python code can use the wrapper instance to send updates:
from typing import Any, Mapping
import proc_wrapper
from proc_wrapper import ProcWrapper
def fun(wrapper: ProcWrapper, cbdata: dict[str, int],
config: Mapping[str, Any]) -> int:
wrapper.update_status(last_status_message="Starting the fun ...")
success_count = 0
error_count = 0
for i in range(100):
try:
do_work()
success_count += 1
except Exception:
error_count += 1
wrapper.update_status(success_count=success_count,
error_count=error_count)
wrapper.update_status(last_status_message="The fun is over.")
return cbdata["a"]
params = ProcWrapperParams()
params.auto_create_task = True
params.auto_create_task_run_environment_name = "production"
params.task_name = "embedded_test"
params.api_key = "YOUR_CLOUDREACTOR_API_KEY"
proc_wrapper = ProcWrapper(params=params)
proc_wrapper.managed_call(fun, {"a": 1, "b": 2})
Example Projects¶
These projects contain sample Tasks that use this library to report their execution status and results to CloudReactor
cloudreactor-python-ecs-quickstart runs python code in a Docker container in AWS ECS Fargate (wrapped mode)
cloudreactor-python-lambda-quickstart runs python code in AWS Lambda (embedded mode)
cloudreactor-java-ecs-quickstart runs Java code in a Docker container in AWS ECS Fargate (wrapped mode)
License¶
This software is dual-licensed under open source (MPL 2.0) and commercial
licenses. See LICENSE for details.
Contributors ✨¶
Thanks goes to these wonderful people (emoji key):
 Jeff Tsay 💻 📖 🚇 🚧 |
 Mike Waldner 💻 |
 Bruno Alla 💻 🤔 📖 |
This project follows the all-contributors specification. Contributions of any kind welcome!
Credits¶
This package was created with Cookiecutter and the browniebroke/cookiecutter-pypackage project template.