Larvata

$?: Most Recent Exit Code

When you quote $*, it will output all of the arguments received as one single string, separated by a space1 regardless of how they were quoted going in, but it will quote that string so that it doesn't get split up later.

The [[ double square brackets ]] work essentially the same as [ single square brackets ], albeit with some more superpowers like more powerful regex support.

The (( double parentheses )) are actually a construct that allow arithmetic inside Bash.

If the results inside are zero, it returns an exit code of 1. (Essentially, zero is "falsey.")

the greater and less-than symbols work just fine inside arithmetic parens.

If the regex works out, the return code of the double square brackets is 0, and thus the function returns 0. If not, everything returns 1. This is a really great way to name regexes.

the stuff immediately after the if can be any command in the whole wide world, as long as it provides an exit code, which is pretty much always.

{% ... %} for Statements

{{ ... }} for Expressions to print to the template output

use a dot (.) to access attributes of a variable

the outer double-curly braces are not part of the variable, but the print statement.

if an object has an item and attribute with the same name. Additionally, the attr() filter only looks up attributes.

Variables can be modified by filters. Filters are separated from the variable by a pipe symbol (|) and may have optional arguments in parentheses.

Multiple filters can be chained

Tests can be used to test a variable against a common expression.

add is plus the name of the test after the variable.

to find out if a variable is defined, you can do name is defined, which will then return true or false depending on whether name is defined in the current template context.

strip whitespace in templates by hand. If you add a minus sign (-) to the start or end of a block (e.g. a For tag), a comment, or a variable expression, the whitespaces before or after that block will be removed

not add whitespace between the tag and the minus sign

mark a block raw

Template inheritance allows you to build a base “skeleton” template that contains all the common elements of your site and defines blocks that child templates can override.

The {% extends %} tag is the key here. It tells the template engine that this template “extends” another template.

access templates in subdirectories with a slash

can’t define multiple {% block %} tags with the same name in the same template

put the name of the block after the end tag for better readability

if the block is replaced by a child template, a variable would appear that was not defined in the block or passed to the context.

setting the block to “scoped” by adding the scoped modifier to a block declaration

Jinja2 functions (macros, super, self.BLOCKNAME) always return template data that is marked as safe.

With the default syntax, control structures appear inside {% ... %} blocks.

the dictsort filter

loop.cycle

use loops recursively

whether the value changed at all,

use it to test if a variable is defined, not empty and not false

Macros are comparable with functions in regular programming languages.

If a macro name starts with an underscore, it’s not exported and can’t be imported.

pass a macro to another macro

caller()

a single trailing newline is stripped if present

other whitespace (spaces, tabs, newlines etc.) is returned unchanged

caller(user)

call(user)

This is a simple dialog rendered by using a macro and a call block.

Filter sections allow you to apply regular Jinja2 filters on a block of template data.

Assignments at top level (outside of blocks, macros or loops) are exported from the template like top level macros and can be imported by other templates.

using namespace objects which allow propagating of changes across scopes

use block assignments to capture the contents of a block into a variable name.

The extends tag can be used to extend one template from another.

Blocks are used for inheritance and act as both placeholders and replacements at the same time.

The include statement is useful to include a template and return the rendered contents of that file into the current namespace

Included templates have access to the variables of the active context by default.

putting often used code into macros

imports are cached and imported templates don’t have access to the current template variables, just the globals by default.

Macros and variables starting with one or more underscores are private and cannot be imported.

By default, included templates are passed the current context and imported templates are not.

Integers and floating point numbers are created by just writing the number down

Everything between two brackets is a list.

Tuples are like lists that cannot be modified (“immutable”).

A dict in Python is a structure that combines keys and values.

// Divide two numbers and return the truncated integer result

(expr) group an expression.

The is and in operators support negation using an infix notation

in Perform a sequence / mapping containment test.

| Applies a filter.

~ Converts all operands into strings and concatenates them.

use inline if expressions.

always an attribute is returned and items are not looked up.

default(value, default_value=u'', boolean=False)¶ If the value is undefined it will return the passed default value, otherwise the value of the variable

dictsort(value, case_sensitive=False, by='key', reverse=False)¶ Sort a dict and yield (key, value) pairs.

format(value, *args, **kwargs)¶ Apply python string formatting on an object

indent(s, width=4, first=False, blank=False, indentfirst=None)¶ Return a copy of the string with each line indented by 4 spaces. The first line and blank lines are not indented by default.

join(value, d=u'', attribute=None)¶ Return a string which is the concatenation of the strings in the sequence.

map()¶ Applies a filter on a sequence of objects or looks up an attribute.

pprint(value, verbose=False)¶ Pretty print a variable. Useful for debugging.

reject()¶ Filters a sequence of objects by applying a test to each object, and rejecting the objects with the test succeeding.

replace(s, old, new, count=None)¶ Return a copy of the value with all occurrences of a substring replaced with a new one.

round(value, precision=0, method='common')¶ Round the number to a given precision

even if rounded to 0 precision, a float is returned.

select()¶ Filters a sequence of objects by applying a test to each object, and only selecting the objects with the test succeeding.

sort(value, reverse=False, case_sensitive=False, attribute=None)¶ Sort an iterable. Per default it sorts ascending, if you pass it true as first argument it will reverse the sorting.

striptags(value)¶ Strip SGML/XML tags and replace adjacent whitespace by one space.

tojson(value, indent=None)¶ Dumps a structure to JSON so that it’s safe to use in <script> tags.

trim(value)¶ Strip leading and trailing whitespace.

unique(value, case_sensitive=False, attribute=None)¶ Returns a list of unique items from the the given iterable

urlize(value, trim_url_limit=None, nofollow=False, target=None, rel=None)¶ Converts URLs in plain text into clickable links.

defined(value)¶ Return true if the variable is defined

in(value, seq)¶ Check if value is in seq.

mapping(value)¶ Return true if the object is a mapping (dict etc.).

number(value)¶ Return true if the variable is a number.

sameas(value, other)¶ Check if an object points to the same memory address than another object

undefined(value)¶ Like defined() but the other way round.

A joiner is passed a string and will return that string every time it’s called, except the first time (in which case it returns an empty string).

namespace(...)¶ Creates a new container that allows attribute assignment using the {% set %} tag

The with statement makes it possible to create a new inner scope. Variables set within this scope are not visible outside of the scope.

activate and deactivate the autoescaping from within the templates

With both trim_blocks and lstrip_blocks enabled, you can put block tags on their own lines, and the entire block line will be removed when rendered, preserving the whitespace of the contents

There’s a lot wrong with this: you could be using the wrong version of code that has exploits, has a bug in it, or worse it could have malware bundled in on purpose—you just don’t know.

Keep Base Images Small

Node.js for example, it includes an extra 600MB of libraries you don’t need.

Use the Builder Pattern

#2: Container Internals

Use a Non-Root User Inside the Container

Make the File System Read-Only

One Process per Container

Don’t Restart on Failure. Crash Cleanly Instead.

Log Everything to stdout and stderr

Use the “Record” Option for Easier Rollbacks

Use Weave Cloud

Use Sidecars for Proxies, Watchers, Etc.

Don’t Use Sidecars for Bootstrapping!

Don’t Use :Latest or No Tag

Readiness and Liveness Probes are Your Friend

Don’t Use type: LoadBalancer

Type: Nodeport Can Be “Good Enough”

Use Static IPs They Are Free!

Map External Services to Internal Ones

Use Helm Charts

All Downstream Dependencies Are Unreliable

Use Plenty of Descriptive Labels

Make Sure Your Microservices Aren’t Too Micro

Use Namespaces to Split Up Your Cluster

Role-Based Access Control

A null queue driver is also included which discards queued jobs.

In your config/queue.php configuration file, there is a connections configuration option.

any given queue connection may have multiple "queues" which may be thought of as different stacks or piles of queued jobs.

each connection configuration example in the queue configuration file contains a queue attribute.

if you dispatch a job without explicitly defining which queue it should be dispatched to, the job will be placed on the queue that is defined in the queue attribute of the connection configuration

pushing jobs to multiple queues can be especially useful for applications that wish to prioritize or segment how jobs are processed

specify which queues it should process by priority.

If your Redis queue connection uses a Redis Cluster, your queue names must contain a key hash tag.

ensure all of the Redis keys for a given queue are placed into the same hash slot

all of the queueable jobs for your application are stored in the app/Jobs directory.

Job classes are very simple, normally containing only a handle method which is called when the job is processed by the queue.

we were able to pass an Eloquent model directly into the queued job's constructor. Because of the SerializesModels trait that the job is using, Eloquent models will be gracefully serialized and unserialized when the job is processing.

When the job is actually handled, the queue system will automatically re-retrieve the full model instance from the database.

The handle method is called when the job is processed by the queue

The arguments passed to the dispatch method will be given to the job's constructor

delay the execution of a queued job, you may use the delay method when dispatching a job.

dispatch a job immediately (synchronously), you may use the dispatchNow method.

specify a list of queued jobs that should be run in sequence.

Deleting jobs using the $this->delete() method will not prevent chained jobs from being processed. The chain will only stop executing if a job in the chain fails.

this does not push jobs to different queue "connections" as defined by your queue configuration file, but only to specific queues within a single connection.

To specify the queue, use the onQueue method when dispatching the job

To specify the connection, use the onConnection method when dispatching the job

defining the maximum number of attempts on the job class itself.

to defining how many times a job may be attempted before it fails, you may define a time at which the job should timeout.

using the funnel method, you may limit jobs of a given type to only be processed by one worker at a time

using the throttle method, you may throttle a given type of job to only run 10 times every 60 seconds.

If an exception is thrown while the job is being processed, the job will automatically be released back onto the queue so it may be attempted again.

dispatch a Closure. This is great for quick, simple tasks that need to be executed outside of the current request cycle

When dispatching Closures to the queue, the Closure's code contents is cryptographically signed so it can not be modified in transit.

Laravel includes a queue worker that will process new jobs as they are pushed onto the queue.

once the queue:work command has started, it will continue to run until it is manually stopped or you close your terminal

they will not notice changes in your code base after they have been started.

during your deployment process, be sure to restart your queue workers.

customize your queue worker even further by only processing particular queues for a given connection

The --once option may be used to instruct the worker to only process a single job from the queue

The --stop-when-empty option may be used to instruct the worker to process all jobs and then exit gracefully.

Daemon queue workers do not "reboot" the framework before processing each job.

Since queue workers are long-lived processes, they will not pick up changes to your code without being restarted.

php artisan queue:restart

The queue uses the cache to store restart signals

the queue workers will die when the queue:restart command is executed, you should be running a process manager such as Supervisor to automatically restart the queue workers.

each queue connection defines a retry_after option. This option specifies how many seconds the queue connection should wait before retrying a job that is being processed.

The --timeout option specifies how long the Laravel queue master process will wait before killing off a child queue worker that is processing a job.

When jobs are available on the queue, the worker will keep processing jobs with no delay in between them.

While sleeping, the worker will not process any new jobs - the jobs will be processed after the worker wakes up again

the numprocs directive will instruct Supervisor to run 8 queue:work processes and monitor all of them, automatically restarting them if they fail.

Laravel includes a convenient way to specify the maximum number of times a job should be attempted.

define a failed method directly on your job class, allowing you to perform job specific clean-up when a failure occurs.

a great opportunity to notify your team via email or Slack.

php artisan queue:retry all

php artisan queue:flush

When injecting an Eloquent model into a job, it is automatically serialized before being placed on the queue and restored when the job is processed

instruct Laravel to create an instance of your application and start executing jobs, this instance will stay alive indefinitely which means the action of starting your Laravel application happens only once when the command was run & the same instance will be used to execute your jobs

This will start an instance of the application, process a single job,

Using queue:listen ensures that a new instance of the app is created for every job, that means you don't have to manually restart the worker in case you made changes to your code, but also means more server resources will be consumed.

the queue:listen command runs the WorkCommand inside a loop

The connection this worker will be pulling jobs from

The queue the worker will use to find jobs

when you do Queue::push() you're actually calling the push method on the queue driver you're using

内网建立所谓的私库，作为代理与外网的公共库同步。

很难做到真正意义上的DevOps to Production

可视化是为了实时展现持续交付流水线执行情况与单元测试的执行报告

通过持续交付流水线串联自动化测试，在测试环境部署成功后触发自动化测试。

测试阶段也需要测试报告的可视化与结果通知

企业的持续交付流水线往往都打不通到生产环境

Service Desk不是某一款软件的名字，而是ITIL（信息技术基础架构库，可认为是ITSM的落地实现）里面承载变更管理与事件管理的工具统称。

构建底层的云平台，是整个DevOps基础架构的基石

架构不是一成不变的，而是应该随着实际需求变化而持续演化，能力也要跟着持续提升。

并行测试的执行环境通过PaaS平台按需自动生成，测试执行完毕后自动销毁。

即使是雷同的项目，在对编译构建上的一些细枝末节的差别也很可能导致它们的持续交付流水线设计非常不一样。

Different programming languages are for different tasks.

Go or C which are compiled to run on bare metal.

To run NodeJS on multiple cores, you have to use something like PM2, but since this you have to keep your code stateless.

Python have very rich and sugary syntax that’s great for working with data while keeping your code small and expressive.

SQL is almost always slower than NoSQL

databases are often read-first or write-first

write-first, just like Cassandra.

store all of your data to your databases and leave nothing at backend

Functional code is stateless by default

It’s better to go for stateless right from the very beginning.

deliver exactly the same responses for same requests.

Sessions? Store them at Redis and allow all of your servers to access it.

Only the first user will trigger a data query, and all others will be receiving exactly the same data straight from the RAM

Only the server output should be cached

Varnish is a great cache option that works with HTTP responses, so it may work with any backend.

a rate limiter – if there’s not enough time have passed since last request, the ongoing request will be denied.

Just set up entry relations and allow your database to calculate external keys for you

Backend should have different responsibilities: hashing, building web pages from data and templates, managing sessions and so on.

a distributed database.

your code has to be stateless

Move anything related to the data to the database.

For load-balancing a database, go for cluster.

DB is balancing requests, as well as your backend.

Users from different continents are separated with DNS.

Keep is scalable, keep is stateless.

Twelve-factor strongly favors languages which provide a REPL shell out of the box, and which make it easy to run one-off scripts.

long-term archival. These archival destinations are not visible to or configurable by the app, and instead are completely managed by the execution environment.

Most significantly, the stream can be sent to a log indexing and analysis system such as Splunk, or a general-purpose data warehousing system such as Hadoop/Hive.

Backing services, such as the app’s database, queueing system, or cache, is one area where dev/prod parity is important

The twelve-factor developer resists the urge to use different backing services between development and production, even when adapters theoretically abstract away any differences in backing services.

declarative provisioning tools such as Chef and Puppet combined with light-weight virtual environments such as Docker and Vagrant allow developers to run local environments which closely approximate production environments.

returning the current job to the work queue

all jobs are reentrant, which typically is achieved by wrapping the results in a transaction, or making the operation idempotent

Processes should also be robust against sudden death, in the case of a failure in the underlying hardware.

a twelve-factor app is architected to handle unexpected, non-graceful terminations

application must also be able to span multiple processes running on multiple physical machines.

The array of process types and number of processes of each type is known as the process formation.