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張 旭

Introduction to GitLab Flow | GitLab - 0 views

  • GitLab flow as a clearly defined set of best practices. It combines feature-driven development and feature branches with issue tracking.
  • In Git, you add files from the working copy to the staging area. After that, you commit them to your local repo. The third step is pushing to a shared remote repository.
  • branching model
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  • The biggest problem is that many long-running branches emerge that all contain part of the changes.
  • It is a convention to call your default branch master and to mostly branch from and merge to this.
  • Nowadays, most organizations practice continuous delivery, which means that your default branch can be deployed.
  • Continuous delivery removes the need for hotfix and release branches, including all the ceremony they introduce.
  • Merging everything into the master branch and frequently deploying means you minimize the amount of unreleased code, which is in line with lean and continuous delivery best practices.
  • GitHub flow assumes you can deploy to production every time you merge a feature branch.
  • You can deploy a new version by merging master into the production branch. If you need to know what code is in production, you can just checkout the production branch to see.
  • Production branch
  • Environment branches
  • have an environment that is automatically updated to the master branch.
  • deploy the master branch to staging.
  • To deploy to pre-production, create a merge request from the master branch to the pre-production branch.
  • Go live by merging the pre-production branch into the production branch.
  • Release branches
  • work with release branches if you need to release software to the outside world.
  • each branch contains a minor version
  • After announcing a release branch, only add serious bug fixes to the branch.
  • merge these bug fixes into master, and then cherry-pick them into the release branch.
  • Merging into master and then cherry-picking into release is called an “upstream first” policy
  • Tools such as GitHub and Bitbucket choose the name “pull request” since the first manual action is to pull the feature branch.
  • Tools such as GitLab and others choose the name “merge request” since the final action is to merge the feature branch.
  • If you work on a feature branch for more than a few hours, it is good to share the intermediate result with the rest of the team.
  • the merge request automatically updates when new commits are pushed to the branch.
  • If the assigned person does not feel comfortable, they can request more changes or close the merge request without merging.
  • In GitLab, it is common to protect the long-lived branches, e.g., the master branch, so that most developers can’t modify them.
  • if you want to merge into a protected branch, assign your merge request to someone with maintainer permissions.
  • After you merge a feature branch, you should remove it from the source control software.
  • Having a reason for every code change helps to inform the rest of the team and to keep the scope of a feature branch small.
  • If there is no issue yet, create the issue
  • The issue title should describe the desired state of the system.
  • For example, the issue title “As an administrator, I want to remove users without receiving an error” is better than “Admin can’t remove users.”
  • create a branch for the issue from the master branch
  • If you open the merge request but do not assign it to anyone, it is a “Work In Progress” merge request.
  • Start the title of the merge request with [WIP] or WIP: to prevent it from being merged before it’s ready.
  • When they press the merge button, GitLab merges the code and creates a merge commit that makes this event easily visible later on.
  • Merge requests always create a merge commit, even when the branch could be merged without one. This merge strategy is called “no fast-forward” in Git.
  • Suppose that a branch is merged but a problem occurs and the issue is reopened. In this case, it is no problem to reuse the same branch name since the first branch was deleted when it was merged.
  • At any time, there is at most one branch for every issue.
  • It is possible that one feature branch solves more than one issue.
  • GitLab closes these issues when the code is merged into the default branch.
  • If you have an issue that spans across multiple repositories, create an issue for each repository and link all issues to a parent issue.
  • use an interactive rebase (rebase -i) to squash multiple commits into one or reorder them.
  • you should never rebase commits you have pushed to a remote server.
  • Rebasing creates new commits for all your changes, which can cause confusion because the same change would have multiple identifiers.
  • if someone has already reviewed your code, rebasing makes it hard to tell what changed since the last review.
  • never rebase commits authored by other people.
  • it is a bad idea to rebase commits that you have already pushed.
  • If you revert a merge commit and then change your mind, revert the revert commit to redo the merge.
  • Often, people avoid merge commits by just using rebase to reorder their commits after the commits on the master branch.
  • Using rebase prevents a merge commit when merging master into your feature branch, and it creates a neat linear history.
  • every time you rebase, you have to resolve similar conflicts.
  • Sometimes you can reuse recorded resolutions (rerere), but merging is better since you only have to resolve conflicts once.
  • A good way to prevent creating many merge commits is to not frequently merge master into the feature branch.
  • keep your feature branches short-lived.
  • Most feature branches should take less than one day of work.
  • If your feature branches often take more than a day of work, try to split your features into smaller units of work.
  • You could also use feature toggles to hide incomplete features so you can still merge back into master every day.
  • you should try to prevent merge commits, but not eliminate them.
  • Your codebase should be clean, but your history should represent what actually happened.
  • If you rebase code, the history is incorrect, and there is no way for tools to remedy this because they can’t deal with changing commit identifiers
  • Commit often and push frequently
  • You should push your feature branch frequently, even when it is not yet ready for review.
  • A commit message should reflect your intention, not just the contents of the commit.
  • each merge request must be tested before it is accepted.
  • test the master branch after each change.
  • If new commits in master cause merge conflicts with the feature branch, merge master back into the branch to make the CI server re-run the tests.
  • When creating a feature branch, always branch from an up-to-date master.
  • Do not merge from upstream again if your code can work and merge cleanly without doing so.
張 旭

Warnings, Notes, & Tips - 0 views

  • AS3 manages topology records globally in /Common, it is required that records only be managed through AS3, as it will treat the records declaratively.
  • If a record is added outside of AS3, it will be removed if it is not included in the next AS3 declaration for topology records (AS3 completely overwrites non-AS3 topologies when a declaration is submitted).
  • using AS3 to delete a tenant (for example, sending DELETE to the /declare/<TENANT> endpoint) that contains GSLB topologies will completely remove ALL GSLB topologies from the BIG-IP.
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  • When posting a large declaration (hundreds of application services in a single declaration), you may experience a 500 error stating that the save sys config operation failed.
  • Even if you have asynchronous mode set to false, after 45 seconds AS3 sets asynchronous mode to true (API swap), and returns an async response.
  • When creating a new tenant using AS3, it must not use the same name as a partition you separately create on the target BIG-IP system.
  • If you use the same name and then post the declaration, AS3 overwrites (or removes) the existing partition completely, including all configuration objects in that partition.
  • use AS3 to create a tenant (which creates a BIG-IP partition), manually adding configuration objects to the partition created by AS3 can have unexpected results
  • When you delete the Tenant using AS3, the system deletes both virtual servers.
  • if a Firewall_Address_List contains zero addresses, a dummy IPv6 address of ::1:5ee:bad:c0de is added in order to maintain a valid Firewall_Address_List. If an address is added to the list, the dummy address is removed.
  • use /mgmt/shared/appsvcs/declare?async=true if you have a particularly large declaration which will take a long time to process.
  • reviewing the Sizing BIG-IP Virtual Editions section (page 7) of Deploying BIG-IP VEs in a Hyper-Converged Infrastructure
  • To test whether your system has AS3 installed or not, use GET with the /mgmt/shared/appsvcs/info URI.
  • You may find it more convenient to put multi-line texts such as iRules into AS3 declarations by first encoding them in Base64.
  • no matter your BIG-IP user account name, audit logs show all messages from admin and not the specific user name.
張 旭

AskF5 | Manual Chapter: Working with Partitions - 0 views

  • During BIG-IP® system installation, the system automatically creates a partition named Common
  • An administrative partition is a logical container that you create, containing a defined set of BIG-IP® system objects.
  • No user can delete partition Common itself.
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  • With respect to permissions, all users on the system except those with a user role of No Access have read access to objects in partition Common, and by default, partition Common is their current partition.
  • The current partition is the specific partition to which the system is currently set for a logged-in user.
  • A partition access assignment gives a user some level of access to the specified partition.
  • assigning partition access to a user does not necessarily give the user full access to all objects in the partition
  • user account objects also reside in partitions
  • when you first install the BIG-IP system, every existing user account (root and admin) resides in partition Common
  • the partition in which a user account object resides does not affect the partition or partitions to which that user is granted access to manage other BIG-IP objects
  • the object it references resides in partition Common
  • a referenced object must reside either in the same partition as the object that is referencing it
張 旭

- 0 views

  • A fast-forward merge can happen when the current branch has no extra commits compared to the branch we’re merging.
  • With a no-fast-forward merge, Git creates a new merging commit on the active branch.
  • We can manually remove the changes we don't want to keep, save the changes, add the changed file again, and commit the changes
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  • A git rebase copies the commits from the current branch, and puts these copied commits on top of the specified branch.
  • The branch that we're rebasing always has the latest changes that we want to keep!
  • A git rebase changes the history of the project as new hashes are created for the copied commits!
  • Rebasing is great whenever you're working on a feature branch, and the master branch has been updated.
  • An interactive rebase can also be useful on the branch you're currently working on, and want to modify some commits.
  • A git reset gets rid of all the current staged files and gives us control over where HEAD should point to.
  • A soft reset moves HEAD to the specified commit (or the index of the commit compared to HEAD)
  • Git should simply reset its state back to where it was on the specified commit: this even includes the changes in your working directory and staged files!
  • By reverting a certain commit, we create a new commit that contains the reverted changes!
  • Performing a git revert is very useful in order to undo a certain commit, without modifying the history of the branch.
  • By cherry-picking a commit, we create a new commit on our active branch that contains the changes that were introduced by the cherry-picked commit.
  • a fetch simply downloads new data.
  • A git pull is actually two commands in one: a git fetch, and a git merge
  • git reflog is a very useful command in order to show a log of all the actions that have been taken
張 旭

Rate Limits - Let's Encrypt - Free SSL/TLS Certificates - 0 views

  • If you have a lot of subdomains, you may want to combine them into a single certificate, up to a limit of 100 Names per Certificate.
  • A certificate with multiple names is often called a SAN certificate, or sometimes a UCC certificate
  • The main limit is Certificates per Registered Domain (20 per week).
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  • A certificate is considered a duplicate of an earlier certificate if they contain the exact same set of hostnames, ignoring capitalization and ordering of hostnames.
  • We also have a Duplicate Certificate limit of 5 certificates per week.
  • a Renewal Exemption to the Certificates per Registered Domain limit.
  • The Duplicate Certificate limit and the Renewal Exemption ignore the public key and extensions requested
  • You can issue 20 certificates in week 1, 20 more certificates in week 2, and so on, while not interfering with renewals of existing certificates.
  • Revoking certificates does not reset rate limits
  • If you’ve hit a rate limit, we don’t have a way to temporarily reset it.
  • get a list of certificates issued for your registered domain by searching on crt.sh
  • Revoking certificates does not reset rate limits
  • If you have a large number of pending authorization objects and are getting a rate limiting error, you can trigger a validation attempt for those authorization objects by submitting a JWS-signed POST to one of its challenges, as described in the ACME spec.
  • If you do not have logs containing the relevant authorization URLs, you need to wait for the rate limit to expire.
  • having a large number of pending authorizations is generally the result of a buggy client
張 旭

Databases and Collections - MongoDB Manual - 0 views

  • MongoDB stores data records as documents (specifically BSON documents) which are gathered together in collections.
  • A database stores one or more collections of documents.
  • In MongoDB, databases hold one or more collections of documents.
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  • If a database does not exist, MongoDB creates the database when you first store data for that database.
  • The insertOne() operation creates both the database myNewDB and the collection myNewCollection1 if they do not already exist.
  • MongoDB stores documents in collections.
  • If a collection does not exist, MongoDB creates the collection when you first store data for that collection.
  • MongoDB provides the db.createCollection() method to explicitly create a collection with various options, such as setting the maximum size or the documentation validation rules.
  • By default, a collection does not require its documents to have the same schema;
  • To change the structure of the documents in a collection, such as add new fields, remove existing fields, or change the field values to a new type, update the documents to the new structure.
  • Collections are assigned an immutable UUID.
  • To retrieve the UUID for a collection, run either the listCollections command or the db.getCollectionInfos() method.
張 旭

Introduction to MongoDB - MongoDB Manual - 0 views

  • MongoDB is a document database designed for ease of development and scaling
  • MongoDB offers both a Community and an Enterprise version
  • A record in MongoDB is a document, which is a data structure composed of field and value pairs.
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  • MongoDB documents are similar to JSON objects.
  • The values of fields may include other documents, arrays, and arrays of documents.
  • reduce need for expensive joins
  • MongoDB stores documents in collections.
  • Collections are analogous to tables in relational databases.
  • Read-only Views
  • Indexes support faster queries and can include keys from embedded documents and arrays.
  • MongoDB's replication facility, called replica set
  • A replica set is a group of MongoDB servers that maintain the same data set, providing redundancy and increasing data availability.
  • Sharding distributes data across a cluster of machines.
  • MongoDB supports creating zones of data based on the shard key.
  • MongoDB provides pluggable storage engine API
張 旭

Extend the Kubernetes API with CustomResourceDefinitions | Kubernetes - 0 views

  • When you create a new CustomResourceDefinition (CRD), the Kubernetes API Server creates a new RESTful resource path for each version you specify.
  • The CRD can be either namespaced or cluster-scoped, as specified in the CRD's scope field
  • deleting a namespace deletes all custom objects in that namespace.
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  • CustomResourceDefinitions themselves are non-namespaced and are available to all namespaces.
  • Custom objects can contain custom fields. These fields can contain arbitrary JSON.
  • When you delete a CustomResourceDefinition, the server will uninstall the RESTful API endpoint and delete all custom objects stored in it
  • CustomResourceDefinitions store validated resource data in the cluster's persistence store, etcd.
  • By default, all unspecified fields for a custom resource, across all versions, are pruned.
  • The field json can store any JSON value, without anything being pruned.
  • Finalizers allow controllers to implement asynchronous pre-delete hooks.
張 旭

ALB vs ELB | Differences Between an ELB and an ALB on AWS | Sumo Logic - 0 views

  • If you use AWS, you have two load-balancing options: ELB and ALB.
  • An ELB is a software-based load balancer which can be set up and configured in front of a collection of AWS Elastic Compute (EC2) instances.
  • The load balancer serves as a single entry point for consumers of the EC2 instances and distributes incoming traffic across all machines available to receive requests.
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  • the ELB also performs a vital role in improving the fault tolerance of the services which it fronts.
  • he Open Systems Interconnection Model, or OSI Model, is a conceptual model which is used to facilitate communications between different computing systems.
  • Layer 1 is the physical layer, and represents the physical medium across which the request is sent.
  • Layer 2 describes the data link layer
  • Layer 3 (the network layer)
  • Layer 7, which serves the application layer.
  • The Classic ELB operates at Layer 4. Layer 4 represents the transport layer, and is controlled by the protocol being used to transmit the request.
  • A network device, of which the Classic ELB is an example, reads the protocol and port of the incoming request, and then routes it to one or more backend servers.
  • the ALB operates at Layer 7. Layer 7 represents the application layer, and as such allows for the redirection of traffic based on the content of the request.
  • Whereas a request to a specific URL backed by a Classic ELB would only enable routing to a particular pool of homogeneous servers, the ALB can route based on the content of the URL, and direct to a specific subgroup of backing servers existing in a heterogeneous collection registered with the load balancer.
  • The Classic ELB is a simple load balancer, is easy to configure
  • As organizations move towards microservice architecture or adopt a container-based infrastructure, the ability to merely map a single address to a specific service becomes more complicated and harder to maintain.
  • the ALB manages routing based on user-defined rules.
  • oute traffic to different services based on either the host or the content of the path contained within that URL.
張 旭

Best practices for building Kubernetes Operators and stateful apps | Google Cloud Blog - 0 views

  • use the StatefulSet workload controller to maintain identity for each of the pods, and to use Persistent Volumes to persist data so it can survive a service restart.
  • a way to extend Kubernetes functionality with application specific logic using custom resources and custom controllers.
  • An Operator can automate various features of an application, but it should be specific to a single application
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  • Kubebuilder is a comprehensive development kit for building and publishing Kubernetes APIs and Controllers using CRDs
  • Design declarative APIs for operators, not imperative APIs. This aligns well with Kubernetes APIs that are declarative in nature.
  • With declarative APIs, users only need to express their desired cluster state, while letting the operator perform all necessary steps to achieve it.
  • scaling, backup, restore, and monitoring. An operator should be made up of multiple controllers that specifically handle each of the those features.
  • the operator can have a main controller to spawn and manage application instances, a backup controller to handle backup operations, and a restore controller to handle restore operations.
  • each controller should correspond to a specific CRD so that the domain of each controller's responsibility is clear.
  • If you keep a log for every container, you will likely end up with unmanageable amount of logs.
  • integrate application-specific details to the log messages such as adding a prefix for the application name.
  • you may have to use external logging tools such as Google Stackdriver, Elasticsearch, Fluentd, or Kibana to perform the aggregations.
  • adding labels to metrics to facilitate aggregation and analysis by monitoring systems.
  • a more viable option is for application pods to expose a metrics HTTP endpoint for monitoring tools to scrape.
  • A good way to achieve this is to use open-source application-specific exporters for exposing Prometheus-style metrics.
張 旭

How Percona XtraBackup Works - 0 views

  • Percona XtraBackup is based on InnoDB‘s crash-recovery functionality.
  • it performs crash recovery on the files to make them a consistent, usable database again
  • InnoDB maintains a redo log, also called the transaction log. This contains a record of every change to InnoDB data.
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  • When InnoDB starts, it inspects the data files and the transaction log, and performs two steps. It applies committed transaction log entries to the data files, and it performs an undo operation on any transactions that modified data but did not commit.
  • Percona XtraBackup works by remembering the log sequence number (LSN) when it starts, and then copying away the data files.
  • Percona XtraBackup runs a background process that watches the transaction log files, and copies changes from it.
  • Percona XtraBackup needs to do this continually
  • Percona XtraBackup needs the transaction log records for every change to the data files since it began execution.
  • Percona XtraBackup uses Backup locks where available as a lightweight alternative to FLUSH TABLES WITH READ LOCK.
  • Locking is only done for MyISAM and other non-InnoDB tables after Percona XtraBackup finishes backing up all InnoDB/XtraDB data and logs.
  • xtrabackup tries to avoid backup locks and FLUSH TABLES WITH READ LOCK when the instance contains only InnoDB tables. In this case, xtrabackup obtains binary log coordinates from performance_schema.log_status
  • When backup locks are supported by the server, xtrabackup first copies InnoDB data, runs the LOCK TABLES FOR BACKUP and then copies the MyISAM tables.
  • the STDERR of xtrabackup is not written in any file. You will have to redirect it to a file, e.g., xtrabackup OPTIONS 2> backupout.log
  • During the prepare phase, Percona XtraBackup performs crash recovery against the copied data files, using the copied transaction log file. After this is done, the database is ready to restore and use.
  • the tools enable you to do operations such as streaming and incremental backups with various combinations of copying the data files, copying the log files, and applying the logs to the data.
  • To restore a backup with xtrabackup you can use the --copy-back or --move-back options.
  • you may have to change the files’ ownership to mysql before starting the database server, as they will be owned by the user who created the backup.
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    "Percona XtraBackup is based on InnoDB's crash-recovery functionality."
張 旭

Incremental Backup - 0 views

  • xtrabackup supports incremental backups, which means that they can copy only the data that has changed since the last backup.
  • You can perform many incremental backups between each full backup, so you can set up a backup process such as a full backup once a week and an incremental backup every day, or full backups every day and incremental backups every hour.
  • each InnoDB page contains a log sequence number, or LSN. The LSN is the system version number for the entire database. Each page’s LSN shows how recently it was changed.
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  • In full backups, two types of operations are performed to make the database consistent: committed transactions are replayed from the log file against the data files, and uncommitted transactions are rolled back.
  • You should use the --apply-log-only option to prevent the rollback phase.
  • An incremental backup copies each page whose LSN is newer than the previous incremental or full backup’s LSN.
  • Incremental backups do not actually compare the data files to the previous backup’s data files.
  • you can use --incremental-lsn to perform an incremental backup without even having the previous backup, if you know its LSN
  • Incremental backups simply read the pages and compare their LSN to the last backup’s LSN.
  • without a full backup to act as a base, the incremental backups are useless.
  • The xtrabackup binary writes a file called xtrabackup_checkpoints into the backup’s target directory. This file contains a line showing the to_lsn, which is the database’s LSN at the end of the backup.
  • from_lsn is the starting LSN of the backup and for incremental it has to be the same as to_lsn (if it is the last checkpoint) of the previous/base backup.
  • If you do not use the --apply-log-only option to prevent the rollback phase, then your incremental backups will be useless.
  • run --prepare as usual, but prevent the rollback phase
  • If you restore it and start MySQL, InnoDB will detect that the rollback phase was not performed, and it will do that in the background, as it usually does for a crash recovery upon start.
  • xtrabackup --prepare --apply-log-only --target-dir=/data/backups/base \ --incremental-dir=/data/backups/inc1
  • The final data is in /data/backups/base, not in the incremental directory.
  • Do not run xtrabackup --prepare with the same incremental backup directory (the value of –incremental-dir) more than once.
  • xtrabackup --prepare --target-dir=/data/backups/base \ --incremental-dir=/data/backups/inc2
  • --apply-log-only should be used when merging all incrementals except the last one.
  • Even if the --apply-log-only was used on the last step, backup would still be consistent but in that case server would perform the rollback phase.
張 旭

Kubernetes 基本概念 · Kubernetes指南 - 0 views

  • Container(容器)是一种便携式、轻量级的操作系统级虚拟化技术。它使用 namespace 隔离不同的软件运行环境,并通过镜像自包含软件的运行环境,从而使得容器可以很方便的在任何地方运行。
  • 每个应用程序用容器封装,管理容器部署就等同于管理应用程序部署。+
  • Pod 是一组紧密关联的容器集合,它们共享 PID、IPC、Network 和 UTS namespace,是 Kubernetes 调度的基本单位。
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  • 进程间通信和文件共享
  • 在 Kubernetes 中,所有对象都使用 manifest(yaml 或 json)来定义
  • Node 是 Pod 真正运行的主机,可以是物理机,也可以是虚拟机。
  • 每个 Node 节点上至少要运行 container runtime(比如 docker 或者 rkt)、kubelet 和 kube-proxy 服务。
  • 常见的 pods, services, replication controllers 和 deployments 等都是属于某一个 namespace 的(默认是 default)
  • node, persistentVolumes 等则不属于任何 namespace
  • Service 是应用服务的抽象,通过 labels 为应用提供负载均衡和服务发现。
  • 匹配 labels 的 Pod IP 和端口列表组成 endpoints,由 kube-proxy 负责将服务 IP 负载均衡到这些 endpoints 上。
  • 每个 Service 都会自动分配一个 cluster IP(仅在集群内部可访问的虚拟地址)和 DNS 名
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    "常见的 pods, services, replication controllers 和 deployments 等都是属于某一个 namespace 的(默认是 default),而 node, persistentVolumes 等则不属于任何 namespace。"
張 旭

mvn clean install - a short guide to Maven - 0 views

  • An equivalent in other languages would be Javascript’s npm, Ruby’s gems or PHP’s composer.
  • Maven expects a certain directory structure for your Java source code to live in and when you later do a mvn clean install , the whole compilation and packaging work will be done for you.
  • any directory that contains a pom.xml file is also a valid Maven project.
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  • A pom.xml file contains everything needed to describe your Java project.
  • Java source code is to be meant to live in the "/src/main/java" folder
  • Maven will put compiled Java classes into the "target/classes" folder
  • Maven will also build a .jar or .war file, depending on your project, that lives in the "target" folder.
  • Maven has the concept of a build lifecycle, which is made up of different phases.
  • clean is not part of Maven’s default lifecycle, you end up with commands like mvn clean install or mvn clean package. Install or package will trigger all preceding phases, but you need to specify clean in addition.
  • Maven will always download your project dependencies into your local maven repository first and then reference them for your build.
  • local repositories (in your user’s home directory: ~/.m2/)
  • clean: deletes the /target folder.
  • mvn clean package
  • mvn clean install
  • package: Converts your .java source code into a .jar/.war file and puts it into the /target folder.
  • install: First, it does a package(!). Then it takes that .jar/.war file and puts it into your local Maven repository, which lives in ~/.m2/repository.
  • calling 'mvn install' would be enough if Maven was smart enough to do reliable, incremental builds.
  • figuring out what Java source files/modules changed and only compile those.
  • developers got it ingrained to always call 'mvn clean install' (even though this increases build time a lot in bigger projects).
  • make sure that Maven always tries to download the latest snapshot dependency versions
張 旭

Trunk-based Development | Atlassian - 0 views

  • Trunk-based development is a version control management practice where developers merge small, frequent updates to a core “trunk” or main branch.
  • Gitflow and trunk-based development. 
  • Gitflow, which was popularized first, is a stricter development model where only certain individuals can approve changes to the main code. This maintains code quality and minimizes the number of bugs.
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  • Trunk-based development is a more open model since all developers have access to the main code. This enables teams to iterate quickly and implement CI/CD.
  • Developers can create short-lived branches with a few small commits compared to other long-lived feature branching strategies.
  • Gitflow is an alternative Git branching model that uses long-lived feature branches and multiple primary branches.
  • Gitflow also has separate primary branch lines for development, hotfixes, features, and releases.
  • Trunk-based development is far more simplified since it focuses on the main branch as the source of fixes and releases.
  • Trunk-based development eases the friction of code integration.
  • trunk-based development model reduces these conflicts.
  • Adding an automated test suite and code coverage monitoring for this stream of commits enables continuous integration.
  • When new code is merged into the trunk, automated integration and code coverage tests run to validate the code quality.
  • Trunk-based development strives to keep the trunk branch “green”, meaning it's ready to deploy at any commit.
  • With continuous integration, developers perform trunk-based development in conjunction with automated tests that run after each committee to a trunk.
  • If trunk-based development was like music it would be a rapid staccato -- short, succinct notes in rapid succession, with the repository commits being the notes.
  • Instead of creating a feature branch and waiting to build out the complete specification, developers can instead create a trunk commit that introduces the feature flag and pushes new trunk commits that build out the feature specification within the flag.
  • Automated testing is necessary for any modern software project intending to achieve CI/CD.
  • Short running unit and integration tests are executed during development and upon code merge.
  • Automated tests provide a layer of preemptive code review.
  • Once a branch merges, it is best practice to delete it.
  • A repository with a large amount of active branches has some unfortunate side effects
  • Merge branches to the trunk at least once a day
  • The “continuous” in CI/CD implies that updates are constantly flowing.
張 旭

Using cache in GitLab CI with Docker-in-Docker | $AYMDEV() - 0 views

  • optimize our images.
  • When you build an image, it is made of multiple layers: we add a layer per instruction.
  • If we build the same image again without modifying any file, Docker will use existing layers rather than re-executing the instructions.
  • ...21 more annotations...
  • an image is made of multiple layers, and we can accelerate its build by using layers cache from the previous image version.
  • by using Docker-in-Docker, we get a fresh Docker instance per job which local registry is empty.
  • docker build --cache-from "$CI_REGISTRY_IMAGE:latest" -t "$CI_REGISTRY_IMAGE:new-tag"
  • But if you maintain a CHANGELOG in this format, and/or your Git tags are also your Docker tags, you can get the previous version and use cache the this image version.
  • script: - export PREVIOUS_VERSION=$(perl -lne 'print "v${1}" if /^##\s\[(\d\.\d\.\d)\]\s-\s\d{4}(?:-\d{2}){2}\s*$/' CHANGELOG.md | sed -n '2 p') - docker build --cache-from "$CI_REGISTRY_IMAGE:$PREVIOUS_VERSION" -t "$CI_REGISTRY_IMAGE:$CI_COMMIT_TAG" -f ./prod.Dockerfile .
  • « Docker layer caching » is enough to optimize the build time.
  • Cache in CI/CD is about saving directories or files across pipelines.
  • We're building a Docker image, dependencies are installed inside a container.We can't cache a dependencies directory if it doesn't exists in the job workspace.
  • Dependencies will always be installed from a container but will be extracted by the GitLab Runner in the job workspace. Our goal is to send the cached version in the build context.
  • We set the directories to cache in the job settings with a key to share the cache per branch and stage.
  • - docker cp app:/var/www/html/vendor/ ./vendor
  • after_script
  • - docker cp app:/var/www/html/node_modules/ ./node_modules
  • To avoid old dependencies to be mixed with the new ones, at the risk of keeping unused dependencies in cache, which would make cache and images heavier.
  • If you need to cache directories in testing jobs, it's easier: use volumes !
  • version your cache keys !
  • sharing Docker image between jobs
  • In every job, we automatically get artifacts from previous stages.
  • docker save $DOCKER_CI_IMAGE | gzip > app.tar.gz
  • I personally use the « push / pull » technique,
  • we docker push after the build, then we docker pull if needed in the next jobs.
張 旭

Syntax - Configuration Language | Terraform | HashiCorp Developer - 0 views

  • the native syntax of the Terraform language, which is a rich language designed to be relatively easy for humans to read and write.
  • Terraform's configuration language is based on a more general language called HCL, and HCL's documentation usually uses the word "attribute" instead of "argument."
  • A particular block type may have any number of required labels, or it may require none
  • ...34 more annotations...
  • After the block type keyword and any labels, the block body is delimited by the { and } characters
  • Identifiers can contain letters, digits, underscores (_), and hyphens (-). The first character of an identifier must not be a digit, to avoid ambiguity with literal numbers.
  • The # single-line comment style is the default comment style and should be used in most cases.
  • he idiomatic style is to use the Unix convention
  • Indent two spaces for each nesting level.
  • align their equals signs
  • Use empty lines to separate logical groups of arguments within a block.
  • Use one blank line to separate the arguments from the blocks.
  • "meta-arguments" (as defined by the Terraform language semantics)
  • Avoid separating multiple blocks of the same type with other blocks of a different type, unless the block types are defined by semantics to form a family.
  • Resource names must start with a letter or underscore, and may contain only letters, digits, underscores, and dashes.
  • Each resource is associated with a single resource type, which determines the kind of infrastructure object it manages and what arguments and other attributes the resource supports.
  • Each resource type is implemented by a provider, which is a plugin for Terraform that offers a collection of resource types.
  • By convention, resource type names start with their provider's preferred local name.
  • Most publicly available providers are distributed on the Terraform Registry, which also hosts their documentation.
  • The Terraform language defines several meta-arguments, which can be used with any resource type to change the behavior of resources.
  • use precondition and postcondition blocks to specify assumptions and guarantees about how the resource operates.
  • Some resource types provide a special timeouts nested block argument that allows you to customize how long certain operations are allowed to take before being considered to have failed.
  • Timeouts are handled entirely by the resource type implementation in the provider
  • Most resource types do not support the timeouts block at all.
  • A resource block declares that you want a particular infrastructure object to exist with the given settings.
  • Destroy resources that exist in the state but no longer exist in the configuration.
  • Destroy and re-create resources whose arguments have changed but which cannot be updated in-place due to remote API limitations.
  • Expressions within a Terraform module can access information about resources in the same module, and you can use that information to help configure other resources. Use the <RESOURCE TYPE>.<NAME>.<ATTRIBUTE> syntax to reference a resource attribute in an expression.
  • resources often provide read-only attributes with information obtained from the remote API; this often includes things that can't be known until the resource is created, like the resource's unique random ID.
  • data sources, which are a special type of resource used only for looking up information.
  • some dependencies cannot be recognized implicitly in configuration.
  • local-only resource types exist for generating private keys, issuing self-signed TLS certificates, and even generating random ids.
  • The behavior of local-only resources is the same as all other resources, but their result data exists only within the Terraform state.
  • The count meta-argument accepts a whole number, and creates that many instances of the resource or module.
  • count.index — The distinct index number (starting with 0) corresponding to this instance.
  • the count value must be known before Terraform performs any remote resource actions. This means count can't refer to any resource attributes that aren't known until after a configuration is applied
  • Within nested provisioner or connection blocks, the special self object refers to the current resource instance, not the resource block as a whole.
  • This was fragile, because the resource instances were still identified by their index instead of the string values in the list.
  •  
    "the native syntax of the Terraform language, which is a rich language designed to be relatively easy for humans to read and write. "
張 旭

Production environment | Kubernetes - 0 views

  • to promote an existing cluster for production use
  • Separating the control plane from the worker nodes.
  • Having enough worker nodes available
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  • You can use role-based access control (RBAC) and other security mechanisms to make sure that users and workloads can get access to the resources they need, while keeping workloads, and the cluster itself, secure. You can set limits on the resources that users and workloads can access by managing policies and container resources.
  • you need to plan how to scale to relieve increased pressure from more requests to the control plane and worker nodes or scale down to reduce unused resources.
  • Managed control plane: Let the provider manage the scale and availability of the cluster's control plane, as well as handle patches and upgrades.
  • The simplest Kubernetes cluster has the entire control plane and worker node services running on the same machine.
  • You can deploy a control plane using tools such as kubeadm, kops, and kubespray.
  • Secure communications between control plane services are implemented using certificates.
  • Certificates are automatically generated during deployment or you can generate them using your own certificate authority.
  • Separate and backup etcd service: The etcd services can either run on the same machines as other control plane services or run on separate machines
  • Create multiple control plane systems: For high availability, the control plane should not be limited to a single machine
  • Some deployment tools set up Raft consensus algorithm to do leader election of Kubernetes services. If the primary goes away, another service elects itself and take over.
  • Groups of zones are referred to as regions.
  • if you installed with kubeadm, there are instructions to help you with Certificate Management and Upgrading kubeadm clusters.
  • Production-quality workloads need to be resilient and anything they rely on needs to be resilient (such as CoreDNS).
  • Add nodes to the cluster: If you are managing your own cluster you can add nodes by setting up your own machines and either adding them manually or having them register themselves to the cluster’s apiserver.
  • Set up node health checks: For important workloads, you want to make sure that the nodes and pods running on those nodes are healthy.
  • Authentication: The apiserver can authenticate users using client certificates, bearer tokens, an authenticating proxy, or HTTP basic auth.
  • Authorization: When you set out to authorize your regular users, you will probably choose between RBAC and ABAC authorization.
  • Role-based access control (RBAC): Lets you assign access to your cluster by allowing specific sets of permissions to authenticated users. Permissions can be assigned for a specific namespace (Role) or across the entire cluster (ClusterRole).
  • Attribute-based access control (ABAC): Lets you create policies based on resource attributes in the cluster and will allow or deny access based on those attributes.
  • Set limits on workload resources
  • Set namespace limits: Set per-namespace quotas on things like memory and CPU
  • Prepare for DNS demand: If you expect workloads to massively scale up, your DNS service must be ready to scale up as well.
張 旭

Installing kubeadm | Kubernetes - 0 views

  • Swap disabled. You MUST disable swap in order for the kubelet to work properly.
  • The product_uuid can be checked by using the command sudo cat /sys/class/dmi/id/product_uuid
  • some virtual machines may have identical values.
  • ...6 more annotations...
  • Kubernetes uses these values to uniquely identify the nodes in the cluster.
  • Make sure that the br_netfilter module is loaded.
  • you should ensure net.bridge.bridge-nf-call-iptables is set to 1 in your sysctl config,
  • kubeadm will not install or manage kubelet or kubectl for you, so you will need to ensure they match the version of the Kubernetes control plane you want kubeadm to install for you.
  • one minor version skew between the kubelet and the control plane is supported, but the kubelet version may never exceed the API server version.
  • Both the container runtime and the kubelet have a property called "cgroup driver", which is important for the management of cgroups on Linux machines.
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