AWS Lambda function/layer version deployment manager.
Project description
Reviser
Reviser is a tool for AWS Lambda function and layer version deployment and alias management specifically for Python runtimes where the actual infrastructure is managed separately, mostly likely by CloudFormation or Terraform. There are a number of ways to manage AWS Lambda functions and layers already, but their generality and all-encompassing approaches don't integrate well with certain workflows and can be overly complex for many needs.
Reviser is scoped to facilitate the deployment and updating of AWS Lambda Python functions and layers for all version-specific configurations, e.g. code bundles, environment variables, memory size, and timeout lengths. The expectation is that functions are created by other means and then configuration for versions is managed with the reviser through an interactive or scripted shell of commands.
- Basic Usage
- Shell Commands
- Configuration Files
- bucket(s)
- AWS region
- targets
- function targets
- run
- Shared Dependencies
- Local Execution
Basic Usage
A project defines one or more lambda function configuration targets in a
lambda.yaml file in the root project directory. The most basic configuration
looks like this:
bucket: name-of-s3-bucket-for-code-uploads
targets:
- kind: function
name: foo-function
This configuration defines a single foo-function lambda function target that
will be managed by reviser. The expectation is that this function exists and
was created by another means, e.g. CloudFormation or Terraform. A bucket must
be specified to indicate where the zipped code bundles will be uploaded prior
to them being applied to the target(s). The bucket must already exist as well.
By default the package will include no external, e.g. pip, package
dependencies. It will search for the first folder in the directory where the
lambda.yaml file is located that contains an __init__.py file, identifying
that folder as a Python source package for the function. It will also look for
a lambda_function.py alongside the lambda.yaml file to serve as the
entrypoint. These will be included in the uploaded and deployed code bundle
when a push or a deploy command is executed. These default settings can
all be configured along with many more as will be outlined below.
To deploy this example project, install the reviser python library and
start the shell with the command reviser in your terminal of choice
in the directory where the lambda.yaml file resides. Docker must be running
and available in the terminal in which you execute this command, as reviser
is a containerized shell environment that runs within a container that mimics
the actual AWS Lambda runtime environment. Then run the push command within
the launched shell to create and upload the bundled source code and publish
a new version of the foo-function lambda function with the uploaded results.
Shell commands
The reviser command starts an interactive shell within a Docker container
compatible with the AWS Python Lambda runtime. This shell contains various
commands for deploying and managing deployments of lambda functions and layers
defined in a project's lambda.yaml configuration file, the format of which
is described later in this document. The shell commands are:
alias
Assign an alias to the specified version of the selected or specified lambda function.
usage: alias [--function FUNCTION] [--yes] [--create] alias version
positional arguments:
alias Name of an existing alias to move to the specified
version, or the name of an alias to create and assign
to the specified function version if the --create flag
is included to allow for creating a new alias.
version Version of the function that the alias should be
assigned to. This will either be an integer value or
$LATEST. To see what versions are available for a given
function use the list command.
optional arguments:
--function FUNCTION The alias command only acts on one function. This can
be achieved either by selecting the function target via
the select command, or specifying the function name to
apply this change to with this flag.
--yes By default this command will require input confirmation
before carrying out the change. Specify this flag to
skip input confirmation and proceed without a breaking
prompt.
--create When specified the alias will be created instead of
reassigned. Use this to create and assign new aliases
to a function. When this flag is not specified, the
command will fail if the alias doesn't exist, which
helps prevent accidental alias creation.
Or it will create a new alias and assign it to the specified version if the --create
flag is included. To assign an existing test alias to version 42 of the selected
function, the command would be:
> alias test 42
If multiple functions are currently selected, use --function=<NAME>
to identify the function to which the alias change will be applied.
bundle
Install dependencies and copies includes into a zipped file ready for deployment.
usage: bundle [--reinstall] [--output OUTPUT]
optional arguments:
--reinstall Add this flag to reinstall dependencies on a repeated
bundle operation. By default, dependencies will remain
cached for the lifetime of the shell to speed up the
bundling process. This will force dependencies to be
installed even if they had been installed previously.
--output OUTPUT, -o OUTPUT
Output the bundled artifacts into the specified output
path.
The resulting zip file is structured correctly to be deployed to the lambda function/layer target via an S3 upload and subsequent publish command.
configs
Display the configs merged from its source file, dynamic values and defaults.
usage: configs
Use this to inspect and validate that the loaded configuration meets expectations when parsed into the reviser shell.
deploy
Upload the bundled contents to the upload S3 bucket and then publish a new version.
usage: deploy [--description DESCRIPTION] [--dry-run]
optional arguments:
--description DESCRIPTION
Specify a message to assign to the version published
by the deploy command.
--dry-run If set, the deploy operation will be exercised without
actually carrying out the actions. This can be useful
to validate the deploy process without side effects.
This will be carried out for each of the lambda targets with that new bundle and any modified settings between the current configuration and that target's existing configuration. This command will fail if a target being deployed has not already been bundled.
exit
Exit the shell and returns to the parent terminal.
usage: exit
help (?)
Display help information on the commands available within the shell.
usage: help
Additional help on each command can be found using the --help flag on the command in question.
list
List versions of the specified lambda targets with info about each version.
usage: list
prune
Remove old function and/or layer versions for the selected targets.
usage: prune [--start START] [--end END] [--dry-run] [-y]
optional arguments:
--start START Keep versions lower (earlier/before) this one. A negative
value can be specified for relative indexing in the same
fashion as Python lists.
--end END Do not prune versions higher than this value. A negative
value can be specified for relative indexing in the same
fashion as Python lists.
--dry-run Echo pruning operation without actually executing it.
-y, --yes Run the prune process without reviewing first.
push
Combined single command for bundling and deploying the selected targets.
usage: push [--reinstall] [--output OUTPUT] [--description DESCRIPTION]
[--dry-run]
optional arguments:
--reinstall Add this flag to reinstall dependencies on a repeated
bundle operation. By default, dependencies will remain
cached for the lifetime of the shell to speed up the
bundling process. This will force dependencies to be
installed even if they had been installed previously.
--output OUTPUT, -o OUTPUT
Output the bundled artifacts into the specified output
path.
--description DESCRIPTION
Specify a message to assign to the version published
by the deploy command.
--dry-run If set, the deploy operation will be exercised without
actually carrying out the actions. This can be useful
to validate the deploy process without side effects.
region
Switch the target region.
usage: region
[{us-east-2,us-east-1,us-west-1,us-west-2,af-south-1,ap-east-1,ap-south-1,ap-northeast-3,ap-northeast-2,ap-southeast-1,ap-southeast-2,ap-northeast-1,ca-central-1,cn-north-1,cn-northwest-1,eu-central-1,eu-west-1,eu-west-2,eu-south-1,eu-west-3,eu-north-1,me-south-1,sa-east-1,us-gov-east-1,us-gov-west-1}]
positional arguments:
{us-east-2,us-east-1,us-west-1,us-west-2,af-south-1,ap-east-1,ap-south-1,ap-northeast-3,ap-northeast-2,ap-southeast-1,ap-southeast-2,ap-northeast-1,ca-central-1,cn-north-1,cn-northwest-1,eu-central-1,eu-west-1,eu-west-2,eu-south-1,eu-west-3,eu-north-1,me-south-1,sa-east-1,us-gov-east-1,us-gov-west-1}
AWS region name for the override. Leave it blank to
return to the default region for the initially loaded
credentials and/or environment variables.
reload
Reload the lambda.yaml configuration file from disk.
usage: reload
select
Allow for selecting subsets of the targets within the loaded configuration.
usage: select [--functions] [--layers] [--exact] [name ...]
positional arguments:
name Specifies the value to match against the function and
layer target names available from the configuration.
This can include shell-style wildcards and will also
match against partial strings. If the --exact flag is
specified, this value must exactly match one of the
targets instead of the default fuzzy matching
behavior.
optional arguments:
--functions, --function, --func, -f
When specified, functions will be selected. This will
default to true if neither of --functions or --layers
is specified. Will default to false if --layers is
specified.
--layers, --layer, -l
When specified, layers will be selected. This will
default to true if neither of --functions or --layers
is specified. Will default to false if --functions is
specified.
--exact Forces the match to be exact instead of fuzzy.
The subsets are fuzzy-matched unless the --exact flag is used.
shell
Macro command to convert to interactive shell operation.
usage: shell
This is a special command to use in run command groups/macros to start interactive command mode for the terminal. Useful when in scenarios where you wish to prefix an interactive session with commonly executed commands. For example, if you want to select certain targets with the select command as part of starting the shell, you could create a run command group/macro in your lambda.yaml that executes the select command and then executes the shell command. This would updated the selection and then with the shell command, start the shell in interactive mode. Without specifying the shell command here, the run command group/macro would just set a selection and then exit.
status
Show the current status information for each of the selected lambda targets.
usage: status [qualifier]
positional arguments:
qualifier Specifies a version or alias to show status for. If not
specified, $LATEST will be used for functions and the latest
version will be dynamically determined for layers.
tail
Tail the logs for the selected lambda functions.
usage: tail
More detail on any of these commands can be found from within the shell by
executing them with the --help flag.
The reviser application also supports non-interactive batch command
execution via run macros that behave similarly to how npm run <command>
commands are defined. For more details see the run attribute section of the
configuration file definitions below.
Configuration Files
Configuration files, named lambda.yaml define the lambda targets to be
managed within a project. The top-level keys in the configuration file are:
bucket(s)
This key defines the bucket or buckets where zipped source bundles will be uploaded before they are deployed to their lambda function and/or layer targets. Basic usage is to specify the bucket as a key:
bucket: bucket-name
It's also possible for multi-account scenarios to specify multiple buckets as a key-value pairing where the keys are the AWS account IDs (as strings) and the values are the bucket names associated with those IDs. In this case the bucket selection is made dynamically based on the AWS session loaded during shell initialization. Specifying multiple buckets looks like:
buckets:
"123456789": bucket-in-account-123456789
"987654321": bucket-in-account-987654321
Multiple region buckets can also be specified using the AWS region as the key:
buckets:
us-east-1: bucket-in-region-us-east-1
us-west-2: bucket-in-region-us-west-2
These can be combined to define buckets for multiple accounts and multiple regions as:
buckets:
"123456789":
us-east-1: bucket-123456789-in-region-us-east-1
us-west-2: bucket-123456789-in-region-us-west-2
"987654321":
us-east-1: bucket-987654321-in-region-us-east-1
us-west-2: bucket-987654321-in-region-us-west-2
AWS region
The AWS region in which the resources reside can be specified at the top
level of the file if desired. It is recommended that the region be specified
within the calling AWS profile if possible for flexibility, but there are
situations where it makes more sense to make it explicit within the
configuration file instead. If no region is found either in the configuration
file or in the AWS profile the us-east-1 region will be used as the default
in keeping with AWS region defaulting conventions. Specify the region with
the top-level key:
region: us-east-2
targets
Targets is where the bulk of the configuration resides. Each item is either of the function or layer kind and has associated configuration and bundling settings according to the type. Common to both function and layer kinds are the keys:
targets[N].kind
As mentioned already, each target must specify its object type using the kind key:
targets:
- kind: function
...
- kind: layer
...
targets[N].name(s)
The name specifies the name of the target object, not the ARN. For example, a function named foo would be represented as:
targets:```
- kind: function
name: foo
A single target can point to multiple functions. This is useful in cases where a single target could be for both development and production functions or where a single code-base is shared across multiple functions for logical or integration reasons. In this case a list of names is supplied instead:
targets:
- kind: function
names:
- foo-devel
- foo-prod
targets[N].region
In the same fashion as regions can be explicitly set as a top-level configuration key, they can also be set on a per-target basis. If set, the target region will take precedence over the top-level value and the profile-specified value. This makes deploying code across regions within a single configuration file possible.
targets[N].dependencies
Dependencies is a list of external dependency sources to install as
site packages in the lambda function or layer. Multiple package managers
are supported and specified by the kind attribute:
targets:
- kind: layer
name: foo
dependencies:
- kind: pip
- kind: pipper
- kind: poetry
Currently pip, pipper and poetry package managers are supported. For any of the
package managers, the dependencies can be specified explicitly with the
package(s) key.
targets:
- kind: layer
name: foo
dependencies:
- kind: pip
packages:
- spam
- hamd
- kind: pipper
package: spammer
It's also possible to specify a file to where the package dependencies have been defined.
targets:
- kind: layer
name: foo
dependencies:
- kind: pip
file: requirements.layer.txt
- kind: pipper
file: pipper.layer.json
If no packages or file is specified, the default file for the given package
manager will be used by default (e.g. requirements.txt for pip,
pipper.json for pipper, and pyproject.toml for poetry).
It is also possible to specify the same kind of package manager multiple times in this list to aggregate dependencies from multiple locations.
targets[N].dependencies.skip
It is possible to specify inline dependencies to skip during the bundling installation process. This can be useful, for example, when a particular dependency is specific to platforms other than the lambda environment. Or perhaps a package like boto3 that is already available in the lambda function should be skipped to save bundling space while still wanting to include it in the packages dependencies for beyond-lambda deployment purposes.
As shown below, specify the packages to skip within the dependency as part of the dependency definition:
targets:
- kind: function
name: foo
dependencies:
- kind: pip
skip:
- boto3
targets[N].dependencies.arguments
The arguments is an optional map of arguments which will be passed to the package
manager during installation.
targets:
- kind: function
name: foo
dependencies:
- kind: pip
arguments:
--arg1: val1
targets[N].dependencies(kind="pipper")
Pipper repositories have additional configuration not associated with pip
packages. To support pipper libraries, there are two additional attributes
that can be specified: bucket and prefix.
The bucket is required as it specifies the S3 bucket used as the package
source and should be read-accessible by the profile invoking reviser.
The prefix is an optional alternate package prefix within the S3 bucket.
Use this only if you are using an alternate prefix with for your pipper
package.
targets:
- kind: layer
name: foo
dependencies:
- kind: pipper
file: pipper.layer.json
bucket: bucket-name-where-pipper-package-resides
prefix: a/prefix/that/is/not/just/pipper
targets[N].dependencies(kind="poetry")
Poetry repositories have additional extras configuration that can be used to
specify optional dependency groups to install in the lambda. This can be useful
to separate dependencies by function.
targets:
- kind: layer
name: foo
dependencies:
- kind: poetry
extras:
- group
targets[N].bundle
The target bundle object contains the attributes that define the bundle that will be created and uploaded to the functions or layers in a given target as part of the deployment process. It's primary purpose is to define what files should be included in the bundling process, which it achieves with the following attributes.
targets[N].bundle.include(s)
The include(s) key is a string or list of Python glob-styled includes
to add to the bundle. If no includes are specified, the default behavior is:
- function targets: copy the first directory found that contains an init.py file.
- layer targets: do not copy anything and assume dependencies are the only files to copy into the bundle.
All paths should be referenced relative to the root path where the
lambda.yaml is located. For a recursive matching pattern, the glob syntax
should be used as **/*.txt or if restricted to a folder inside of the root
directory then folder/**/*.txt. To include the entire contents of a
directory, specify the path to the folder.
targets:
- kind: function
name: foo
bundle:
includes:
# This is shorthand for "foo_library/**/*"
- foo_library
# All Python files in the "bin/" folder recursively.
- bin/**/*.py
# All Jinja2 files in the root directory that begin "template_".
- template_*.jinja2
targets[N].bundle.exclude(s)
The exclude(s) key is an optional one that is also a string or list of
Python glob-styled paths to remove from the matching include(s). These
are applied to the files found via the includes and do not need to be
comprehensive of all files in the root directory. Building on the example
from above:
targets:
- kind: function
name: foo
bundle:
includes:
# This is shorthand for "foo_library/**/*"
- foo_library
# All Python files in the "bin/" folder recursively.
- bin/**/*.py
# All Jinja2 files in the root directory that begin "template_".
- template_*.jinja2
exclues:
- template_local.jinja2
- template_testing.jinja2
This example would remove two of the template file matches from the includes from the files copied into the bundle for deployment.
All __pycache__, *.pyc and .DS_Store files/directories are
excluded from the copying process in all cases and do not need to be
specified explicitly.
targets[N].bundle.exclude_package(s)
The package_exclude(s) key is an optional one that is also a string or list of
Python glob-styled paths. However, these are for paths to exclude when adding
site-packages to the bundle. Building on the example from above:
targets:
- kind: function
name: foo
bundle:
includes:
# This is shorthand for "foo_library/**/*"
- foo_library
# All Python files in the "bin/" folder recursively.
- bin/**/*.py
# All Jinja2 files in the root directory that begin "template_".
- template_*.jinja2
exclues:
- template_local.jinja2
- template_testing.jinja2
package_excludes:
- foo/foo_windows.py
dependencies:
- kind: pip
This example would not include the site-packages/foo/foo_windows.py from the
bundled zip file for the lambda function. In this case, the reason for omitting
this file is that "Windows" code isn't needed in a linux runtime, so you want to
save some space. This is more likely useful for large packages that include
unneeded components, and it is desirable to save the space. This should be used
very carefully as it can cause external libraries to fail.
targets[N].bundle.omit_package(s)
There can be cases where dependencies install dependencies of their own that
you may not want copied over to the bundle. The most common case is a
dependency that requires boto3, which is available by default in lambda
functions already. In that case it can be useful to list site packages that
should not be copied into the bundle but may have been installed as a side
effect of the dependency installation process.
targets:
- kind: function
name: foo
bundle:
omit_package: boto3
dependencies:
- kind: pip
# Installs a package that requires boto3, which is therefore installed
# into the site-packages bundle directory as a result.
# https://github.com/awslabs/aws-lambda-powertools-python
package: aws-lambda-powertools
In the above example aws-lambda-powertools causes boto3 to be installed
as well. However, since lambda functions have boto3 installed by default,
it's possible to omit that package from the bundling process so that it isn't
installed twice.
Note, however, that installing boto3 directly in a bundle can be beneficial
because it gives you the ability to install the version that is compatible
with your given source code and dependencies. The boto3 version on the lambda
function can be aged and stale.
targets[N].bundle.handler
This attribute only applies to function targets and gives the location of
file and function entrypoint for the lambda function(s) in the target. The
format matches the expected value for lambda functions, which is
<filename_without_extension>.<function_name>.
targets:
- kind: function
name: foo
bundle:
handler: function:main
In this case the bundler would expect to find function.py in the top-leve
directory alongside lambda.yaml and inside it there would be a
main(event, context) function that would be called when the function(s)
are invoked.
If this value is omitted, the default value of lambda_function.lambda_handler
will be used as this matches the AWS lambda Python function documentation.
function targets
In addition to the common attributes described above that are shared between both function and layer targets, there are a number of additional attributes that apply only to function targets. These are:
(function) targets[N].image
Specifies the configuration of the image for image based lambda functions.
This cannot be used with targets[N].bundle. With the exception of uri
all subfields are optional.
image:
uri: 123456789012.dkr.ecr.us-west-2.amazonaws.com/repo:tag
entrypoint: /my/entrypoint
cmd:
- params
- to
- entrypoint
workingdir: /the/working/dir
(function) targets[N].image.uri
The image uri for the function's image. This must be a ECR uri that resides within the same region as the lambda function. If the lambda function is deployed to a single region this can be configured with a string:
uri: 123456789012.dkr.ecr.us-west-2.amazonaws.com/repo:tag
If the lambda function is deployed to multiple regions it can be configured with a dictionary mapping region names to images.
uri:
us-west-2: 123456789012.dkr.ecr.us-west-2.amazonaws.com/repo:tag
us-east-2: 123456789012.dkr.ecr.us-east-2.amazonaws.com/repo:tag
(function) targets[N].image.entrypoint
A custom entrypoint to use for the image. If this is not specified the entrypoint of the image will be used. This can be specified as a list or as a single string that will be treated as a list with one element.
entrypoint: /my/entrypoint
or
entrypoint:
- /my/entrypoint
(function) targets[N].image.cmd
A custom command to use for the image. If this is not specified the default command of the image will be used. This can be specified as a list or as a single string that will be treated as a list with one element.
cmd: a_command
or
cmd:
- a_command
- with
- multiple
- words
(function) targets[N].image.workingdir
A custom working directory to set for the image. If this is not specified the default working directory of the image will be used.
workingdir: /my/working/dir
(function) targets[N].layer(s)
Specifies one or more layers that should be attached to the targeted function(s). Layers can be specified as fully-qualified ARNs for externally specified layers, e.g. a layer created in another AWS account, or by name for layers specified within the account and layers defined within the targets of the configuration file.
targets:
- kind: function
name: foo
layer: arn:aws:lambda:us-west-2:999999999:layer:bar
...
or for multiple layers:
targets:
- kind: function
name: foo
layers:
# A layer defined in another account is specified by ARN.
- arn:aws:lambda:us-west-2:999999999:layer:bar
# A layer in this account is specified by name. This layer may also be
# a target in this configuration file.
- baz
...
- kind: layer
name: baz
...
By default, deployments will use the latest available version of each layer, but this can be overridden by specifying the layer ARN with its version:
targets:
- kind: function
name: foo
layer: arn:aws:lambda:us-west-2:999999999:layer:bar:42
...
In the above example the layer will remain at version 42 until explicitly modified in the configuration file.
Layers can also be defined as objects instead of attributes. The two-layer example from above could be rewritten as:
targets:
- kind: function
name: foo
layers:
- arn: arn:aws:lambda:us-west-2:999999999:layer:bar
- name: baz
...
When specified as an object with attributes, there are a number of additional
attributes that can be specified as well. First, version can be specified
as a separate key from the arn or name, which in many cases can make it easier
to work with than appending it to the end of the arn or function itself for
programmatic/automation:
targets:
- kind: function
name: foo
layers:
- arn: arn:aws:lambda:us-west-2:999999999:layer:bar
version: 42
- name: baz
version: 123
...
Next is that the layer objects accept only and except keys that can be
used to attach the layers to certain functions in the target and not others.
This can be useful in cases where development and production targets share
a lot in common, but perhaps point to different versions of a layer or perhaps
separate development and production layers entirely. It can also be useful
when a target of functions share a common codebase but don't all need the
same dependencies. For performance optimization, restricting the layer
inclusions only to those that need the additional dependencies can be
beneficial.
The only and except attributes can be specified as a single string
or a list of strings that match against unix pattern matching. For example,
expanding on the example from above:
targets:
- kind: function
names:
- foo-devel
- foo-devel-worker
- foo-prod
- foo-prod-worker
layers:
- name: baz-devel
only: foo-devel*
- name: baz-devel-worker
only: foo-devel-worker
- name: baz-prod
only: foo-prod*
- name: baz-prod-worker
only: foo-prod-worker
...
this example shows 4 layers that are conditionally applied using the only
keyword. The example could be rewritten with the except key instead:
targets:
- kind: function
names:
- foo-devel
- foo-devel-worker
- foo-prod
- foo-prod-worker
layers:
- name: baz-devel
except: foo-prod*
- name: baz-devel-worker
except:
- foo-prod*
- foo-devel
- name: baz-prod
except: foo-devel*
- name: baz-prod-worker
except:
- foo-devel*
- foo-prod
...
And either way works. The two (only and except) can also be combined
when that makes more sense. For example, the baz-devel-worker from above
could also be written as:
- name: baz-devel-worker
only: foo-devel*
except: foo-devel
Note that if only is specified it is processed first and then except is
removed from the matches found by only.
(function) targets[N].memory
This specifies the function memory in megabytes either as an integer or
a string with an MB suffix.
targets:
- kind: function
name: foo
memory: 256MB
(function) targets[N].timeout
This specifies the function timeout in seconds either as an integer or
a string with an s suffix.
targets:
- kind: function
name: foo
timeout: 42s
(function) targets[N].variable(s)
Variables contains a list of environment variables to assign to the function.
They can be specified simply with as a string <KEY>=<value> syntax:
targets:
- kind: function
name: foo
variable: MODE=read-only
Here a single environment variable is specified that maps "MODE" to the
value "ready-only". A more programmatic-friendly way is to specify the
name and value as attributes of a variable:
targets:
- kind: function
name: foo
variables:
- name: MODE
value: read-only
Some environment variables may be managed through other means, e.g.
terraform that created the function in the first place or another command
interface used to update the function. For those cases, the preserve
attribute should be set to true and no value specified.
targets:
- kind: function
name: foo
variables:
- name: MODE
preserve: true
In this case the MODE environment variable value will be preserved between
function deployments to contain the value that was already set.
Finally, variables support the same only and exclude attributes that
are found for target layers so that environment variables can be specified
differently for subsets of targets.
The only and except attributes can be specified as a single string
or a list of strings that match against unix pattern matching. For example,
expanding on the example from above:
targets:
- kind: function
names:
- foo-prod
- foo-devel
variables:
- name: MODE
value: write
only: '*prod'
- name: MODE
value: read-only
except: '*prod'
(function) targets[N].ignore(s)
Ignores allows you to specify one or more configuration keys within a function target that should be ignored during deployments. For cases where any of the configuration values:
memorytimeoutvariables
are managed by external systems, they can be specified by the ignores to prevent changes being applied by reviser.
targets:
- kind: function
name: foo
ignores:
- memory
- timeout
run
The run attribute contains an optional object of batch non-interactive commands to run when the shell is called with that run key. This is useful for orchestrating actions for CI/CD purposes as the commands will be processed within a shell environment without user prompts and then the shell will exit when complete without waiting for additional input.
run:
deploy-prod:
- select function *prod
- push --description="($CI_COMMIT_SHORT_SHA): $CI_COMMIT_TITLE"
- alias test -1
targets:
- kind: function
names:
- foo-prod
- foo-devel
In the example above, the deploy-prod run command macro/group would start
the shell and then non-interactively execute the three commands in order
to first select the foo-prod function, then to build and deploy that function
with a description created from CI environment variables and finally move the
test alias to the newly deployed version using a negative version index of
-1. After those three commands are executed reviser will exit the
shell automatically, successfully ending that process.
There is also a special shell command that can be used in run command
macros/groups that will start the shell in interactive mode. This is useful
for using run command macros/groups for pre-configuration during startup of
the interactive shell. Building on the previous example,
run:
deploy-prod:
- select function *prod
- push --description="($CI_COMMIT_SHORT_SHA): $CI_COMMIT_TITLE"
- alias test -1
devel:
- select * *devel
- bundle
- shell
targets:
- kind: function
names:
- foo-prod
- foo-devel
- kind: layer
names:
- bar-devel
- bar-prod
here we've added a devel run command macro/group that will select the devel
function and layer and bundle those but not deploy them. After that's complete
the shell command will kick off the interactive session and ask for user
input. The benefit of this particular run command macro/group is to select
the development targets and pre-build them to cache the dependencies for the
shell user while they continue to develop and deploy the source code to the
function.
Shared Dependencies
It is possible to share dependencies across targets. This is useful if the dependencies are the same but other configurations differ. The configuration will look something like this:
dependencies:
# Each shared dependency must be named, but the name can be any valid yaml key that
# you want.
shared_by_my_foo_and_bar:
- kind: pip
file: requirements.functions.txt
shared_by_others:
- kind: pip
file: requirements.layer.txt
targets:
- kind: function
names:
- foo-prod
- foo-devel
timeout: 30s
memory: 256
dependencies: shared_by_my_foo_and_bar
- kind: function
names:
- bar-prod
- bar-devel
timeout: 500s
memory: 2048
dependencies: shared_by_my_foo_and_bar
- kind: function
names:
- baz-prod
- baz-devel
timeout: 10s
memory: 128
dependencies: shared_by_others
- kind: layer
names:
- spam-prod
- spam-devel
dependencies: shared_by_others
Shared dependencies will be installed once reused by each target configured to use it. Each name shared dependency has the same structure and available options of a regular target dependencies definition.
Local Execution
When running reviser in your current environment instead of launching the shell within
a new container, you will want to use the command reviser-shell. This is the local
version of the CLI that is meant to be used within a suitable container environment
that mimics the lambda runtime environment. It is merely a change in entrypoint, and
has all the shell functionality described for the reviser command above.
Also, to run the reviser-shell successfully, you must install the extra shell
dependencies with the installation:
$ pip install reviser[shell]
Without the shell extras install, the reviser-shell will fail. This is how you would
use reviser in a containerized CI environment as well.
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