[Introduction To Docker And Using Percona Server For MongoDB
Toccara Delacerda
Data at rest encryption for the WiredTiger storage engine in MongoDB wasintroduced in MongoDB Enterprise version 3.2 to ensure that encrypted datafiles can be decrypted and read by parties with the decryption key.
You can only enable data at rest encryption and provide all encryption settings on an empty database, when you start the mongod instance for the first time. You cannot enable or disable encryption while the Percona Server for MongoDB server is already running and / or has some data. Nor can you change the effective encryption mode by simply restarting the server. Every time you restart the server, the encryption settings must be the same.
The data encryption at rest in Percona Server for MongoDB is introduced in version 3.6 to be compatible with data encryption at rest interface in MongoDB. In the current release of Percona Server for MongoDB, the data encryption at rest does not include support for Amazon AWS key management service. Instead, Percona Server for MongoDB is integrated with HashiCorp Vault.
Starting with release 6.0.2-1, Percona Server for MongoDB supports the secure transfer of keys using Key Management Interoperability Protocol (KMIP). This allows users to store encryption keys in their favorite KMIP-compatible key manager when they set up encryption at rest.
Integration with an external key server (recommended). Percona Server for MongoDB is integrated with HashiCorp Vault for this purpose and supports the secure transfer of keys using Key Management Interoperability Protocol (KMIP).
Note that you can use only one of the key management options at a time. However, you can switch from one management option to another (e.g. from a keyfile to HashiCorp Vault). Refer to Migrating from Key File Encryption to HashiCorp Vault Encryption section for details.
Starting from version 3.6, Percona Server for MongoDB also encrypts rollback files when data at rest encryption is enabled. To inspect the contents of these files, use perconadecrypt. This is a tool that you run from the command line as follows:
As initially pointed out, the choices are limited when it comes to open-source solutions driven by the community . Notable among these are Percona Server for MongoDB and FerretDB, which is compatible with MongoDB.
In this blog, as we delve into the case study of a self-managed MongoDB deployment, our primary focus will be on Percona Server for MongoDB. However, stay tuned for a detailed exploration of FerretDB in an upcoming blog post.
Once the EC2 instances are set up in the specified regions, we can proceed with the steps to deploy a replica set using Percona Server for MongoDB. Detailed steps are given in percona docs -server-for-mongodb/5.0/install/index.html
To start migration using MongoPush, HummingBird, or MongoShake, there are several prerequisites to fulfill. Given that the MongoPush repository is now private, follow the steps below to retrieve the binary and begin its use
To sum it up, MongoPush operates by replicating data from the source cluster to the target cluster in batches, mirroring the actions of a secondary node in a replica set. The following image illustrates the overall process, showcasing how data is replicated in multiple parallel tasks or batches
While numerous free solutions exist, we prefer Percona Monitoring and Management because it closely matches Atlas in offering comprehensive metrics and an integrated alerting system. You can learn more by going here
It is crucial for database administrators to avoid performance or memory issues. Tools such as Prometheus and Grafana can help you monitor your database cluster performance. Prometheus is an open-source monitoring and alerting platform that collects and stores metrics in time-series data. Grafana is an open-source web application for interactive visualization and analysis. It allows you to ingest data from a vast number of data sources, query this data, and display it on customizable charts for easy analysis. It is also possible to set alerts so you can quickly and easily be notified of unexpected behavior. Using them together allows you to collect, monitor, analyze, and visualize the data from your MongoDB instance.
In this tutorial, you will set up a MongoDB database and monitor it with Grafana using Prometheus as a data source. To accomplish this, you will configure the MongoDB exporter as a Prometheus target so that Prometheus can scrape your database metrics and make them available for Grafana.
Prometheus is an open-source systems monitoring and alerts toolkit that collects and stores metrics as time-series data. That is, the metrics information is stored with the timestamp at which it was recorded. In this step, you will install Prometheus and configure it to run as a service.
With this code, you configure Prometheus to use the files listed in the ExecStart block to run the service. The service file tells systemd to run Prometheus as the prometheus user with the configuration file /etc/prometheus/prometheus.yml and to store its data in the /var/lib/prometheus directory. You also configure Prometheus to run on port 9090. (The details of systemd service files are beyond the scope of this tutorial, but you can learn more at Understanding Systemd Units and Unit Files.)
Prometheus works by scraping targets to collect metrics. In this step, you will install the MongoDB exporter and configure it as a Prometheus target so that Prometheus can collect the data from your MongoDB instance.
You set the MONGODB_URI to specify the mongodb instance that uses the authentication credentials you set earlier (the test user and testing password). 27017 is the default port for a mongodb instance. When you set the environment variable, it takes precedence over the profile stored in the configuration file.
This service file tells systemd to run MongoDB exporter as a service under the prometheus user. ExecStart will run the mongodb_exporter binary from usr/local/bin/. For more about systemd service files, check out Understanding Systemd Units and Unit Files.
Note: If you are using a remote server, you can view the targets by navigating to _server_ip:9090/targets. You could also use port-forwarding to view the targets locally. To do this, open a new terminal on your local computer and enter the following command:
In this step, you installed the MongoDB exporter and configured it as a Prometheus target to collect metrics. Next, you will create a MongoDB dashboard in the Grafana web console to view and analyze these metrics.
Next, you will import the MongoDB Overview dashboard for Grafana. You can import the dashboard by uploading a JSON file or by importing a dashboard ID, which you can find in the Grafana product documents for dashboards. Here, you will use the dashboard ID to import the dashboard.
Now an Options page will open, where you can provide a name for the dashboard, select the folder for the dashboard, and select a data source. You can leave the dashboard and folder names as the default. For the data source, choose Prometheus. Once you have filled in the options, click on Import.
Your dashboard will show real-time updates of your MongoDB database, including command operations, connections, cursors, document operations, and queued operations. (For additional details, check out the Percona documentation for the MongoDB Overview dashboard.)
In this article, you set up a Grafana dashboard to monitor Prometheus metrics for your MongoDB database, which enables you to monitor your database via a GUI dashboard. First, you installed Prometheus and configured the MongoDB exporter. Then, you added Prometheus as a data source in Grafana, where you could monitor and visualize data from your MongoDB instance.
To use these images, you can either access them directly from theseregistries or push them into your OpenShift Container Platform container image registry. Additionally,you can create an ImageStream that points to the image,either in your container image registry or at the external location. Your OpenShift Container Platformresources can then reference the ImageStream. You can findexampleImageStream definitions for all the provided OpenShift Container Platform images.
You can configure MongoDB with an ephemeral volume or a persistent volume.The first time you use the volume, the database is created along with thedatabase administrator user. Afterwards, the MongoDB daemon starts up. If youare re-attaching the volume to another container, then the database, databaseuser, and the administrator user are not created, and the MongoDB daemon starts.
OpenShift Container Platform uses Software Collections (SCLs)to install and launch MongoDB. If you want to execute a MongoDB command inside ofa running container (for debugging), you must invoke it using bash.
You can now run mongo commands from the bash shell to start a MongoDBinteractive session and perform normal MongoDB operations. For example, toswitch to the sampledb database and authenticate as the database user:
You must specify the user name, password, database name, and admin password.If you do not specify all four, the pod will fail to start and OpenShift Container Platformwill continuously try to restart it.
The administrator user name is set to admin and you must specify its passwordby setting the MONGODB_ADMIN_PASSWORD environment variable. This process isdone upon database initialization.
Changing database passwords directly in MongoDB causes a mismatch between thevalues stored in the variables and the actual passwords. Whenever a databasecontainer starts, it resets the passwords to the values stored in theenvironment variables.
To change these passwords, update one or both of the desired environmentvariables for the related deployment configuration(s) using the oc set envcommand. If multiple deployment configurations utilize these environmentvariables, for example in the case of an application created from a template,you must update the variables on each deployment configuration so that thepasswords are in sync everywhere. This can be done all in the same command:
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