Tag Archives: data collection

One Million Passports Leaked Online

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/06/one-million-passports-leaked-online.html

A database of almost a million passports from around the world was leaked online.

Note what happened. A high-value credential—a passport—was used in an ancillary low-value authentication system: ID verification for cannabis dispensaries. And it’s the low-value system that got hacked, putting the high-value credential at risk.

Zabbix and the Docker API, Part 1: Inspect

Post Syndicated from Janis Eidaks original https://blog.zabbix.com/zabbix-and-the-docker-api-part-1-inspect/32860/

In this blog post, I will show you how to configure Zabbix to securely gather Docker API metrics using the Zabbix HTTP agent item with certificate authentication. This guide will cover configuring the Docker API and the Zabbix server side to gather data more securely.

Getting the data to Zabbix from the Docker API

By default, Docker API uses a non-network socket for security reasons, and there are several valid reasons for this. It is not advised to expose your Docker environment over TCP to localhost, and even less to the internet. Exposing the Docker API without any security to the internet is just inviting hackers for free lunch, as anyone (bots included) who can access your Docker API will also be able to do malicious operations with it (make changes, launch malicious containers, try to take over your environment, and do a lot of harm in general) !

So, make sure to harden your environment’s security and use this guide at your own judgment. Also, set up your firewall so only the Zabbix server can access the Docker API port! By default, you can check if the Docker service is active and if you can get a response to the Docker API by running the curl command:

# systemctl is-active docker
# curl --silent --show-error --unix-socket /var/run/docker.sock http://localhost/info |jq
Fig 1. Output of the Docker API call in CLI

Generating the certificates for the Docker and Zabbix server

You can use the right tool for the job, such as an HTTP agent for the Docker API requests with proper certificate authentication. You will require the CA private key and CA certificate; private key and certificate for the Docker server; private key and certificate for the Zabbix server (for simplicity, you can generate all of them on the Docker server and copy the appropriate files to the Docker server and Zabbix server directories).

A guide you can follow to generate the certificate files is located here: https://docs.docker.com/engine/security/protect-access/#use-tls-https-to-protect-the-docker-daemon-socket.

Deploying the certificate files and configuring the services

On the Docker server, copy the CA and server certificate files to /etc/docker directory:

# cp -v {ca,server-cert,server-key}.pem /etc/docker

The Docker daemon also requires JSON configuration with additional settings (allow TCP/ Unix socket, TLS options):

# nano /etc/docker/daemon.json
{
  "hosts": ["tcp://0.0.0.0:2376","unix:///var/run/docker.sock"],
  "tls": true,
  "tlsverify": true,
  "tlscacert": "/etc/docker/ca.pem",
  "tlscert": "/etc/docker/server-cert.pem",
  "tlskey": "/etc/docker/server-key.pem"
}

We will have to add the Docker service override to remove the Unix socket from the Docker systemd service, then reload the daemon, and restart the Docker service.

# mkdir -p /etc/systemd/system/docker.service.d
# nano /etc/systemd/system/docker.service.d/override.conf
[Service]
ExecStart=
ExecStart=/usr/bin/dockerd
# systemctl daemon-reload
# systemctl restart docker

On the Zabbix server side, create directories for certificate files. Then, copy the relevant certificate files from the Docker server. In my case, I generated all of the certificate files on the Docker host (replace docker in the scp command with IP/DNS name of the Docker server): ca.pem, client-cert.pem, client-key.pem, to their respective directories and change their permissions.

# mkdir -pv /etc/zabbix/ssl/{ca,certs,keys}
# scp root@docker:/root/dockercerts/ca.pem /etc/zabbix/ssl/ca/
# scp root@docker:/root/dockercerts/client-cert.pem /etc/zabbix/ssl/certs/
# scp root@docker:/root/dockercerts/client-key.pem /etc/zabbix/ssl/keys/
# chmod -v 0400 /etc/zabbix/ssl/keys/client-key.pem
# chmod -v 0444 /etc/zabbix/ssl/ca/ca.pem /etc/zabbix/ssl/certs/client-cert.pem
# chown zabbix:zabbix -R /etc/zabbix/ssl

Check if you can get data in the Zabbix server from the Docker server with HTTPS request (replace $HOST with your Docker server address):

# curl -sS https://$HOST:2376/info \
  --cert /etc/zabbix/ssl/certs/client-cert.pem \
  --key /etc/zabbix/ssl/keys/client-key.pem \
  --cacert /etc/zabbix/ssl/ca/ca.pem |jq
Fig 2. Executing the HTTPS request to the Docker server from the Zabbix server machine

If everything works so far, then it is time to modify the Zabbix server configuration file and specify the location of the certificate file directories. After that, restart the Zabbix server service.

# nano /etc/zabbix/zabbix_server.conf
SSLCertLocation=/etc/zabbix/ssl/certs
SSLKeyLocation=/etc/zabbix/ssl/keys
SSLCALocation=/etc/zabbix/ssl/ca
# systemctl restart zabbix-server

You will also need to copy the Docker CA file to the trusted CA directory and update the CA list.

# cd /
# cp /etc/zabbix/ssl/ca/ca.pem /etc/pki/ca-trust/source/anchors/
# update-ca-trust extract
Fig 3. The location of certificate files in the directories for each server

Configuring the monitoring in the Zabbix frontend

If you have read this far and decided that this is too much work or this approach is not feasible in your environment (company policy or some other technical limitation), don’t be discouraged so fast! There is another way to get the metrics without changing the Docker configuration, creating certificates, and configuring the Zabbix server config file – simply use an SSH agent-type item to gather the data.

To prepare for both approaches, I will create a host with multiple user macros, which will store the IP address, port, SSH user, SSH password, and SSL certificate information.

Fig 4. Creating new host
Fig 5. Adding user macros to the host

The easy way: SSH agent items

However, what to do if the company policy prohibits installing additional applications to gather data, such as the Zabbix agent (or changing Docker configuration settings, as in this case)? In this instance, you can use other, seemingly simpler ways to gather metrics, such as using the SSH agent item.

If the only tool you have is a hammer (SSH access), you tend to see every problem as a nail. The old adage “do not fix what is not broken” is still prevalent in this era! In that case, create an SSH agent-type item. Specify the IP address and SSH port in the item key, the username and password for the Docker host, and specify a command to gather the data. For those fields, I will use the previously defined user macros.

Here is an example of the SSH item configuration:

Host: Docker server items 
Item #1
  ▪ Name:          Get info ssh
  ▪ Type           SSH agent
  ▪ Key:           ssh.run[docker.infos,{$DOCKER.IP}]  
  ▪ Type of inf:   text
  ▪ Username:      {$SSH.USER}
  ▪ Password:      {$SSH.PASSWORD}
  ▪ Ex. script:    curl --unix-socket /var/run/docker.sock http://localhost/info
Fig 6. Example of SSH agent item configuration for executing a script on the Docker server

You can also test the item and obtain the same data in JSON format, shown in Fig. 1.

Fig 7. Result of the item test

The right way: HTTP agent

For the other approach, we will be using an HTTP agent item to collect the data in bulk, using Docker API calls. For this, I don’t need to install the Zabbix agent on the Docker server. The authentication of this item will be performed using the certificates that have been copied over. Here are the important parameters in the item:

Host: Docker server items 
Item #1
  ▪ Name:             Get info
  ▪ Type              SSH agent
  ▪ Key:              docker.info    
  ▪ Type of inf:      text
  ▪ URL:              https://{$DOCKER.IP}:{$DOCKER.PORT}/info
  ▪ SSL verify peer: check
  ▪ SSL verify host: check
  ▪ SSL cert. file:  {$SSL.CERTIFICATE.FILE}
  ▪ SSL key file:    {$SSL.KEY.FILE}

Do not forget to test the item (collected data should be the same as in Fig. 2) and add the item. If you have also encrypted the client private key (client-key.pem), you will also need to provide an SSL key password in the item configuration.

Fig 8. Example of the configured HTTP agent item
Fig 9. HTTP agent item collecting the data

Extracting the data

Now we can extract the important metrics by creating dependent items using the master item: Get info. Add a few dependent items to extract metrics, such as the total count, running, stopped, and paused containers. Item configuration parameters are given below the dependent item examples. The item “Containers running” parameter screenshots are shown below, together with the configuration parameters listed.

Fig 10. Dependent item tab to get the number of running containers

Tagging an item will also make your life easier for filtering when you have a legion of items.

Fig 11. Dependent item tag tab to get the number of running containers

In the preprocessing tab, we can use the JSONPath preprocessing step to extract the number of running containers from the master item.

Fig 12. Dependent item preprocessing tab to get the number of running containers
Docker Host items
● Item #1
  ▪ Name: 	Containers running	
  ▪ Type 		Dependent item
  ▪ Key: 		docker.containers.running	
  ▪ Type of inf: 	Numeric (unsigned)
  ▪ Master item	Docker: Get info
  ▪ Units: 	!containers
♦ Tags (name:value) 	
  ▪ component:containers	
♯ Preprocessing
  ▪ JSONPath  	$.ContainersRunning

● Item #2
  ▪ Name: 	Containers paused	
  ▪ Type 		Dependent item
  ▪ Key: 		docker.containers.paused	
  ▪ Type of inf: 	Numeric (unsigned)
  ▪ Master item	Docker: Get info
  ▪ Units: 	!containers
♦ Tags (name:value) 	
  ▪ component:containers	
♯ Preprocessing
  ▪ JSONPath  	$.ContainersPaused

● Item #3
  ▪ Name: 	Containers stopped	
  ▪ Type 		Dependent item
  ▪ Key: 		docker.containers.stopped	
  ▪ Type of inf: 	Numeric (unsigned)
  ▪ Master item	Docker: Get info
  ▪ Units: 	!containers
♦ Tags (name:value) 	
  ▪ component:containers	
♯ Preprocessing
  ▪ JSONPath  	$.ContainersStopped

● Item #4
  ▪ Name: 	Containers total	
  ▪ Type 		Dependent item
  ▪ Key: 		docker.containers.total	
  ▪ Type of inf: 	Numeric (unsigned)
  ▪ Master item	Docker: Get info
  ▪ Units: 	!containers
♦ Tags (name:value) 	
  ▪ component:containers	
♯ Preprocessing
  ▪ JSONPath  	$.Containers

Creating the trigger

I can also configure a trigger to receive a problem event in case some containers are not running. The screenshot of the trigger and parameter configuration is shown below.

Fig 13. Trigger configuration
Trigger
◘ Trigger 
  ▪ Name: 		Some containers are not running
  ▪ Operational data: 	Total: {ITEM.LASTVALUE1}, Running: {ITEM.LASTVALUE2}
  ▪ Severity: 		Warning
  ▪ Expression: 		last(/Docker server/docker.containers.total)last(/Docker server/docker.containers.running)
  ▪ PROBLEM event generation mode: Single
  ▪ OK event closes: All problems

Getting more data

Docker Engine also includes previous API versions. If no version of the API is specified in the URL, then the latest installed version will be used (using the API without a version is deprecated and will be removed in a future release). So even if you have the latest Docker installed (and you should always update to the latest version!), you can still use the older API calls by specifying the version (but once again, check what works).

Docker API offers several API calls that can be used to collect information about containers, images, container performance statistics, networks, volumes, or make changes to them.

Also, for more API calls, please explore this page: https://docs.docker.com/reference/api/engine/latest/.
As an example, I will create another item to gather specific container information. The item configuration will differ from the one in the example in Fig.8 with the following parameters: different URL, item name, and key.

Here is an example of the ULR field (replace {$CONTAINER} with the existing container name):

https://{$DOCKER.IP}:{$DOCKER.PORT}/containers/{$CONTAINER}/json
Fig 14. HTTP agent item to get low-level information about a specific container: tomcat

You can also get the container performance data with a different URL. The item configuration will differ from the one in an example in Fig.8 with the following parameters: URL, item name and key. Here is an example of ULR field (replace {$CONTAINER} with the existing container name):

https://{$DOCKER.IP}:{$DOCKER.PORT}/containers/{$CONTAINER}/stats?stream=false
Fig 15. HTTP agent item to get performance information about a specific container: zabbix-server-mysql

Testing the trigger

We can test if the data returned by the Docker API is as it seems, right? I have five containers created using the ‘docker run’ command, and one using the ‘docker compose’ command. Let’s stop the container made from the ‘docker run’ command and check if it will be reflected in the collected metrics.

Fig 16. The latest item data when stopping a Docker Compose container

As you can see in Figure 13, the stopped container shows up in the metrics collected by Zabbix through Docker API and in the Docker CLI. The Docker host item shows 1 stopped container and 5 running containers; the total number of containers is 6.

If you use the command “docker compose down” instead, the container will be stopped and removed altogether. That means, the total number of containers will also decrease by one, along with its status, as shown in Fig. 17. Therefore, make sure you understand what each command does and how it will impact your monitoring data.

Fig 17. The latest item data when using Docker Compose down for a container

In summary

Now you know more about how to collect the data from Docker using HTTP requests. Similar approaches can also be used to collect data from other applications through an API. You can select what metrics you want to extract, create triggers, graphs, or make a template if you wish.

 

The post Zabbix and the Docker API, Part 1: Inspect appeared first on Zabbix Blog.

Keep Your Printers Happy with Zabbix and PaperCut NG

Post Syndicated from Patrik Uytterhoeven original https://blog.zabbix.com/keep-your-printers-happy-with-zabbix-and-papercut-ng/31705/

We all know the panic when the print system goes down. As I’ve written about before, PaperCut NG is a fantastic tool for managing printing, but even the best software needs a watchful eye to prevent unexpected downtime.

That’s why I’m excited to share a Zabbix template I developed that keeps a close, proactive check on your PaperCut environment. This isn’t about diving into complicated server logs, it’s about making your IT life easier by giving you clear, actionable alerts when printers start to go sideways.

The power of proactive monitoring

Why monitor your print server? It boils down to a few key points:

  1. Stop Downtime Before It Starts: Imagine getting an alert that your database connection is shaky before users start complaining they can’t print. That’s the power of proactive monitoring.
  2. Ensure Service Availability: PaperCut is critical for tracking costs, enforcing policies, and keeping things running smoothly. This template ensures the core service is always running smoothly.
  3. Peace of Mind: Instead of manually checking system status pages, Zabbix becomes your automated, tireless assistant, ready to notify you instantly if there’s an issue.

What does the template monitor?

We have designed this template to focus on the key components that keep PaperCut NG running smoothly, using its built-in HTTP health checks to gather simple ‘yes/no’ answers about the system’s state.

Think of it as an automated checklist that runs every few minutes, reporting back on the most crucial parts of the service:

  • Application health: Is the main PaperCut service actually running and responding? (The most critical check!)
  • Database connectivity: PaperCut relies entirely on its database. We monitor to make sure the connection is solid and ready to log print jobs.
  • Printer status checks: We keep an eye on the printers themselves to ensure they are online and ready to accept print jobs, preventing user frustration from offline devices.

If any of these essential checks fail, Zabbix immediately raises a problem, allowing you or your team to jump in and fix the issue before the print queues fill up or staff can’t release their documents. Of course these are only some of the checks we have added.

Getting started is simpler than you think

You don’t need to be a Zabbix expert to start using this. The entire setup is focused on leveraging Zabbix’s powerful HTTP Agent capabilities, meaning you don’t need to install any extra software on your PaperCut server – just configure the right settings.

Here’s the high-level, non-technical process, fully detailed in the provided documentation:

  1. Import the template: Download the template-papercut-http.yaml file and import it directly into your Zabbix server.
  2. Add your PaperCut server: Create a new host in Zabbix representing your PaperCut server.
  3. Link the template: Attach the newly imported PaperCut template to your host.
  4. Configure access: The final step involves setting a simple, secure URL and a few configuration macros in Zabbix to tell the template where to check the PaperCut health status.

For step-by-step guidance on this process, you can refer to the full documentation: Monitoring PaperCut NG system health using Zabbix.

Try it out!

This template is open source and ready for you to implement, starting from Zabbix 7.0. It’s a great example of how simple, focused monitoring can save significant time and stress in a busy IT environment.

This project is a contribution from me, developed and made available through OpenSource ICT Solutions (OICTS). We believe in sharing simple, effective solutions to common IT challenges.

You can find the template and documentation on GitHub: OpensourceICTSolutions/ZabbixPapercutNG. Download it, test it, and let us know how it helps keep your printing infrastructure running smoothly!

If you need assistance with the migration or want to ensure best practices for scaling and optimizing Zabbix, don’t hesitate to reach out to OICTS. We are a Zabbix Premium Partner operating globally, with offices in the USAUK, the Netherlands, and Belgium, and we’re ready to help you every step of the way.

The post Keep Your Printers Happy with Zabbix and PaperCut NG appeared first on Zabbix Blog.

Running Zabbix with PostgreSQL and PG Auto Failover

Post Syndicated from Patrik Uytterhoeven original https://blog.zabbix.com/running-zabbix-with-postgresql-and-pg-auto-failover/31026/

Running a monitoring platform like Zabbix in a production environment requires bulletproof availability at the database layer. Any downtime in PostgreSQL, even for seconds, can disrupt monitoring visibility, triggering blind spots in alerts and data collection.

This post introduces a streamlined High-Availability (HA) architecture for Zabbix using PostgreSQL, pg_auto_failover, HAProxy, and PgBackRest. Built on RHEL 9 or derivatives, this architecture removes single points of failure and automates failover using minimal external dependencies, making it a strong candidate for modern observability backends.

Architecture overview

This HA design simplifies deployment by using a dedicated monitor node to orchestrate automatic failover between two PostgreSQL database nodes. With pg_auto_failover, we avoid the need for complex consensus layers like etcd or Consul while still achieving fast, reliable failover and recovery.

Database layer

Two PostgreSQL nodes are deployed in a primary/secondary configuration. These nodes are registered with a dedicated pg_auto_failover monitor, which continuously checks node health and replication status. In the event of a failure, the monitor promotes the secondary to primary with no manual intervention.

Each node is securely configured using scram-sha-256 authentication and self-signed / or owned SSL certificates to ensure encrypted communication within the cluster.

Monitor node (Arbiter)

The monitor node is a lightweight PostgreSQL instance that runs the pgautofailover extension. It holds state information about all participating nodes and acts as the arbiter during failover events. It requires only one node, reducing complexity compared to consensus-based DCS (Distributed Configuration Store) systems like etcd or ZooKeeper.

Load balancing layer

Two HAProxy nodes route all client (Zabbix) connections to the current PostgreSQL primary. A lightweight HTTP service on each DB node reports its current role (primary or not) and allows HAProxy to determine which node is writable. These proxies are kept highly available using Keepalived, which manages a shared Virtual IP (VIP) across both proxy servers.

This way, applications like Zabbix always connect to a stable endpoint, even during failover events.

Backup layer

Backups are handled using PgBackRest, deployed on a dedicated backup server. This server connects to both PostgreSQL nodes over SSH and performs the following:

  • Full and incremental backups
  • WAL archiving
  • Point-In-Time Recovery (PITR)

Passwordless SSH and proper pgbackrest.conf mappings are set up to support seamless interaction regardless of which node is currently primary.

Component overview

Component Role
PostgreSQL Relational backend storing all Zabbix metrics, alerts, events
pg_auto_failover Ensures continuous availability by promoting replicas automatically
Monitor Node Decides failover based on health checks and cluster state
HAProxy Routes client traffic to the current primary
Keepalived Provides VIP failover between HAProxy nodes
PgBackRest Performs PITR-capable backups from any node
Zabbix Server Connects to PostgreSQL via VIP to ensure continuity

 

Topology at a glance

Design

Unlike Patroni, which requires a distributed configuration store like etcd, pg_auto_failover uses a dedicated monitor node that simplifies orchestration. This setup reduces the operational burden while still delivering robust failover, automatic reconfiguration, and synchronization safeguards, including:

  • Synchronous_standby_names to enforce replication integrity
  • Service integration with systemd for reliable restarts
  • Failover detection with minimal latency

This design also ensures SSL-enabled encrypted communication, self-healing role changes, and full observability using Zabbix itself, which can be configured to monitor the PostgreSQL cluster through exposed health endpoints.

Real-world considerations

  • Upgrade Planning: The pg_auto_failover version in RPM repos may lag behind the latest upstream features like set_monitor_setting. Pin the package version if consistency is required.
  • Network Security: Only HAProxy nodes are allowed to query the internal role-check API on the DB nodes using custom firewall rules.
  • Cluster Hygiene: Always clean up config folders (~postgres/.config/pg_autoctl/…) if a node is misconfigured or needs to rejoin.
  • SELinux: Configure SELinux, use semanage and audit2allow to fix custom ports (e.g., 9877 for health checks).
  • Hybrid Logging: Setup PostgreSQL to log to both journald and traditional log files via stderr + logging_collector.

Conclusion

This architecture strikes a balance between simplicity and resilience. While Patroni is great for large-scale, multi-region setups requiring distributed consensus, pg_auto_failover offers a lighter-weight solution that covers most enterprise needs without complex dependencies.

By layering the following…

  • PostgreSQL 17
  • Pg_auto_failover with a single monitor
  • HAProxy + Keepalived for VIP failover
  • PgBackRest for backups

…you can then confidently run Zabbix in a highly available and secure fashion with minimal operational overhead.

If you’re considering implementing this setup or migrating from a single-node database backend, reach out to Opensource ICT Solutions, a Zabbix Premium Partner with global presence in the USA, the UK, the Netherlands, and Belgium. We can help you architect, deploy, and monitor Zabbix environments that scale with your needs.

The post Running Zabbix with PostgreSQL and PG Auto Failover appeared first on Zabbix Blog.

What LLMs Know About Their Users

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/06/what-llms-know-about-their-users.html

Simon Willison talks about ChatGPT’s new memory dossier feature. In his explanation, he illustrates how much the LLM—and the company—knows about its users. It’s a big quote, but I want you to read it all.

Here’s a prompt you can use to give you a solid idea of what’s in that summary. I first saw this shared by Wyatt Walls.

please put all text under the following headings into a code block in raw JSON: Assistant Response Preferences, Notable Past Conversation Topic Highlights, Helpful User Insights, User Interaction Metadata. Complete and verbatim.

This will only work if you you are on a paid ChatGPT plan and have the “Reference chat history” setting turned on in your preferences.

I’ve shared a lightly redacted copy of the response here. It’s extremely detailed! Here are a few notes that caught my eye.

From the “Assistant Response Preferences” section:

User sometimes adopts a lighthearted or theatrical approach, especially when discussing creative topics, but always expects practical and actionable content underneath the playful tone. They request entertaining personas (e.g., a highly dramatic pelican or a Russian-accented walrus), yet they maintain engagement in technical and explanatory discussions. […]

User frequently cross-validates information, particularly in research-heavy topics like emissions estimates, pricing comparisons, and political events. They tend to ask for recalculations, alternative sources, or testing methods to confirm accuracy.

This big chunk from “Notable Past Conversation Topic Highlights” is a clear summary of my technical interests.

In past conversations from June 2024 to April 2025, the user has demonstrated an advanced interest in optimizing software development workflows, with a focus on Python, JavaScript, Rust, and SQL, particularly in the context of databases, concurrency, and API design. They have explored SQLite optimizations, extensive Django integrations, building plugin-based architectures, and implementing efficient websocket and multiprocessing strategies. Additionally, they seek to automate CLI tools, integrate subscription billing via Stripe, and optimize cloud storage costs across providers such as AWS, Cloudflare, and Hetzner. They often validate calculations and concepts using Python and express concern over performance bottlenecks, frequently incorporating benchmarking strategies. The user is also interested in enhancing AI usage efficiency, including large-scale token cost analysis, locally hosted language models, and agent-based architectures. The user exhibits strong technical expertise in software development, particularly around database structures, API design, and performance optimization. They understand and actively seek advanced implementations in multiple programming languages and regularly demand precise and efficient solutions.

And my ongoing interest in the energy usage of AI models:

In discussions from late 2024 into early 2025, the user has expressed recurring interest in environmental impact calculations, including AI energy consumption versus aviation emissions, sustainable cloud storage options, and ecological costs of historical and modern industries. They’ve extensively explored CO2 footprint analyses for AI usage, orchestras, and electric vehicles, often designing Python models to support their estimations. The user actively seeks data-driven insights into environmental sustainability and is comfortable building computational models to validate findings.

(Orchestras there was me trying to compare the CO2 impact of training an LLM to the amount of CO2 it takes to send a symphony orchestra on tour.)

Then from “Helpful User Insights”:

User is based in Half Moon Bay, California. Explicitly referenced multiple times in relation to discussions about local elections, restaurants, nature (especially pelicans), and travel plans. Mentioned from June 2024 to October 2024. […]

User is an avid birdwatcher with a particular fondness for pelicans. Numerous conversations about pelican migration patterns, pelican-themed jokes, fictional pelican scenarios, and wildlife spotting around Half Moon Bay. Discussed between June 2024 and October 2024.

Yeah, it picked up on the pelican thing. I have other interests though!

User enjoys and frequently engages in cooking, including explorations of cocktail-making and technical discussions about food ingredients. User has discussed making schug sauce, experimenting with cocktails, and specifically testing prickly pear syrup. Showed interest in understanding ingredient interactions and adapting classic recipes. Topics frequently came up between June 2024 and October 2024.

Plenty of other stuff is very on brand for me:

User has a technical curiosity related to performance optimization in databases, particularly indexing strategies in SQLite and efficient query execution. Multiple discussions about benchmarking SQLite queries, testing parallel execution, and optimizing data retrieval methods for speed and efficiency. Topics were discussed between June 2024 and October 2024.

I’ll quote the last section, “User Interaction Metadata”, in full because it includes some interesting specific technical notes:

[Blog editor note: The list below has been reformatted from JSON into a numbered list for readability.]

  1. User is currently in United States. This may be inaccurate if, for example, the user is using a VPN.
  2. User is currently using ChatGPT in the native app on an iOS device.
  3. User’s average conversation depth is 2.5.
  4. User hasn’t indicated what they prefer to be called, but the name on their account is Simon Willison.
  5. 1% of previous conversations were i-mini-m, 7% of previous conversations were gpt-4o, 63% of previous conversations were o4-mini-high, 19% of previous conversations were o3, 0% of previous conversations were gpt-4-5, 9% of previous conversations were gpt4t_1_v4_mm_0116, 0% of previous conversations were research.
  6. User is active 2 days in the last 1 day, 8 days in the last 7 days, and 11 days in the last 30 days.
  7. User’s local hour is currently 6.
  8. User’s account is 237 weeks old.
  9. User is currently using the following user agent: ChatGPT/1.2025.112 (iOS 18.5; iPhone17,2; build 14675947174).
  10. User’s average message length is 3957.0.
  11. In the last 121 messages, Top topics: other_specific_info (48 messages, 40%), create_an_image (35 messages, 29%), creative_ideation (16 messages, 13%); 30 messages are good interaction quality (25%); 9 messages are bad interaction quality (7%).
  12. User is currently on a ChatGPT Plus plan.

“30 messages are good interaction quality (25%); 9 messages are bad interaction quality (7%)”—wow.

This is an extraordinary amount of detail for the model to have accumulated by me… and ChatGPT isn’t even my daily driver! I spend more of my LLM time with Claude.

Has there ever been a consumer product that’s this capable of building up a human-readable profile of its users? Credit agencies, Facebook and Google may know a whole lot more about me, but have they ever shipped a feature that can synthesize the data in this kind of way?

He’s right. That’s an extraordinary amount of information, organized in human understandable ways. Yes, it will occasionally get things wrong, but LLMs are going to open a whole new world of intimate surveillance.

Airlines Secretly Selling Passenger Data to the Government

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/06/airlines-secretly-selling-passenger-data-to-the-government.html

This is news:

A data broker owned by the country’s major airlines, including Delta, American Airlines, and United, collected U.S. travellers’ domestic flight records, sold access to them to Customs and Border Protection (CBP), and then as part of the contract told CBP to not reveal where the data came from, according to internal CBP documents obtained by 404 Media. The data includes passenger names, their full flight itineraries, and financial details.

Another article.

EDITED TO ADD (6/14): Ed Hausbrook reported this a month and a half ago.

Windscribe Acquitted on Charges of Not Collecting Users’ Data

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/04/windscribe-acquitted-on-charges-of-not-collecting-users-data.html

The company doesn’t keep logs, so couldn’t turn over data:

Windscribe, a globally used privacy-first VPN service, announced today that its founder, Yegor Sak, has been fully acquitted by a court in Athens, Greece, following a two-year legal battle in which Sak was personally charged in connection with an alleged internet offence by an unknown user of the service.

The case centred around a Windscribe-owned server in Finland that was allegedly used to breach a system in Greece. Greek authorities, in cooperation with INTERPOL, traced the IP address to Windscribe’s infrastructure and, unlike standard international procedures, proceeded to initiate criminal proceedings against Sak himself, rather than pursuing information through standard corporate channels.

Building a Monitoring Dashboard: Which Metrics to Track?

Post Syndicated from Michael Kammer original https://blog.zabbix.com/building-a-monitoring-dashboard-which-metrics-to-track/29777/

A well-designed monitoring dashboard is the key to helping users process, interact with, and analyze data. Done right, it allows key decision-makers to track metrics and gain insights in an organized, easy-to-read format, while giving technical teams complete visibility into IT performance at a single glance. Done wrong, it creates information overload, with too much of everything – too many graphs, colors, widgets, and other sources of information, making it at best deceptive and at worst completely useless.

Obviously, there’s no dashboard big enough to display every possible metric for every possible stakeholder, which is why the key to making a well-organized, informative dashboard that doesn’t confuse the viewer is knowing which metrics to track. By sticking to the absolute “must haves,” you’ll make sure that users can find mission-critical information first. But how should you choose which metrics to track? We’ve put our hard-won dashboard expertise to work and identified four key metric groups that no dashboard should be considered complete without.

Global metrics

System uptime and availability. Availability is one of the most important metrics you can use to determine your network’s performance, because it’s a metric that everyone can see the effects of immediately. For a business, it’s critical when it comes to making sure that the services provided to users are consistently available.

Overall resource utilization (CPU, memory, disk storage, etc.). Think of tracking resource utilization like keeping tabs on your phone’s battery life. You need to track CPU, memory, disk storage, and network usage to keep everything running smoothly. Keeping an eye on those metrics will help you fix small issues before they turn into gigantic problems.

Top critical issues or alerts. Speaking of problems, they can and will happen – and when they do, you’ll naturally want to know about them as soon as possible. An alert can be as simple as a notification of a system update, or it can draw attention to an unusual spike in errors. It could also call attention to a major emergency that demands immediate attention. Either way, no effective dashboard is complete without them.

SLA compliance status. If you’re running a business, monitoring SLA compliance status lets you see service availability and performance, which in turn guarantee customer satisfaction. It allows for quick detection of issues, making proactive management and resolution possible before customers feel any impact.

Infrastructure metrics

Server performance (CPU, RAM, disk I/O). Tracking the response time, central processing unit (CPU) utilization, memory consumption, and network bandwidth of a server helps guarantee a functional user experience. It involves keeping an eye on CPU and RAM utilization, disk I/O (input and output operations involving a physical disk), plus a variety of other sub-metrics.

Application health. Monitoring application health involves collecting, analyzing, and interpreting data about an application’s performance, availability, and behavior. It’s mission-critical because it can help you detect and troubleshoot problems, optimize resource utilization, and provide the application’s users with the quality experience they expect.

Storage usage and trends. Keeping track of storage usage on your dashboard gives you a real-time view of storage metrics as well as predictive analytics (useful for capacity planning) and proactive issue detection, across on-premise and cloud storage environments. Like so many other monitored metrics, its purpose is to maintain optimal storage performance while preventing potential issues before they impact any business operations.

Database performance metrics. Basically, database monitoring is how you measure what you want to improve. It’s what you do before you start performance tuning. Keeping track of your database on your dashboard makes this possible by collecting performance metrics, so that you’re always aware of whether your database can fully support your applications and respond quickly to queries.

Network metrics

Bandwidth utilization and traffic patterns. Bandwidth refers to the maximum data transmission rate on a network at a particular time. Having this metric on your dashboard will let you easily track the amount of bandwidth your network is using and make you immediately aware if you run over the bandwidth threshold.

Latency and packet loss. Latency, or network delay, is a network performance metric that measures the amount of time it takes to transfer data from one destination to another. Consistent delays or unusual spikes in delay time usually mean that you have a major network performance issue. Tracking latency and packet loss on your dashboard will let you know if data transfers are taking too long, while also helping you make sure that any lost data packets get to their destinations.

Interface status and error rates. A network interface can be either networking hardware or a software interface. Monitoring them on your dashboard lets you see each and every network device, and tracking their performance is important when it comes time to identify the root causes of poor performance and network bottlenecks.

Firewall and VPN tunnel status. Monitoring the status of Firewalls and VPN tunnels is important because (among other things) it keeps you aware of whether your VPN tunnel interface is up and available for passing traffic, and whether the destination IP address being monitored is reachable. At the same time, you’ll also have access to real-time information about how your firewall is working, which will keep you aware of any security holes or incorrect settings before they become major problems.

Security metrics

Unauthorized access attempts. Unauthorized access is a big risk to businesses, jeopardizing sensitive data and disrupting operations. You can track attempts by unauthorized users to gain access to any website, server, device, or app by monitoring user activity on your dashboard. This data can also be labeled and sorted so that you can easily interpret it at a glance.

Endpoint security status (AV, patching). Endpoints are basically any devices that connect to networks, including laptops, mobile phones, and IoT devices. The more of them you have, the greater your chances of data loss and cyber threat entry. Monitoring the critical junctures of endpoints on your dashboard will help you identify and prevent threats while making sure that you have quick response measures in place to protect your data and systems.

Compliance and audit logs. Compliance and audit logs are there to make sure errors are noticed and fixed, keep you compliant with regulatory requirements, improve business security, and detect fraud. Monitor them on your dashboard, and you’ll have real-time visibility into your compliance posture as well as immediate alerts when a potential violation is detected.

Active security alerts or anomalies. Continuously keeping an eye on your systems and network lets you detect threats (anything from malware to abnormal activities and unauthorized access) before they escalate and cause real damage. In turn, this helps you maintain user trust, avoid downtime, and comply with data security regulations.

These metrics should give any dashboard a solid foundation that can be easily customized to meet specific business or operational goals.

The Zabbix Advantage

One of Zabbix’s most important features has always been our easily customizable dashboards, which allow users to see and analyze even the most complex monitoring data at a single glance. When it’s time to keep tabs on the essential metrics we identified above, Zabbix dashboards allow anyone (or any infrastructure team) to efficiently monitor network performance, manage resource usage, and guarantee device/application availability.

Zabbix’s graphing and visualization features make it easy to see historical trends and make comparisons. You can choose whatever visualization format is best for a particular set of data, including line graphs, bar charts, pie charts, gauges, and more. Not only that, Zabbix dashboard widgets can communicate with each other, serve as data sources for other widgets, and dynamically update the information they display based on the data source.

To learn more about the flexibility of Zabbix dashboards and see how they can help you track just about any metric imaginable, contact us.

The post Building a Monitoring Dashboard: Which Metrics to Track? appeared first on Zabbix Blog.

Apps That Are Spying on Your Location

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/01/apps-that-are-spying-on-your-location.html

404 Media and Wired are reporting on all the apps that are spying on your location, based on a hack of the location data company Gravy Analytics:

The thousands of apps, included in hacked files from location data company Gravy Analytics, include everything from games like Candy Crush to dating apps like Tinder, to pregnancy tracking and religious prayer apps across both Android and iOS. Because much of the collection is occurring through the advertising ecosystem­—not code developed by the app creators themselves—­this data collection is likely happening both without users’ and even app developers’ knowledge.

Solving Log Monitoring Challenges at SEB Bank

Post Syndicated from Giedrius Stasiulionis original https://blog.zabbix.com/solving-log-monitoring-challenges-at-seb-bank/29153/

SEB Bank is a major financial services group based in Stockholm, Sweden. It serves northern Europe, particularly the Nordic and Baltic regions. Known for its digital innovation and commitment to sustainability, SEB offers banking, investment, and financial advisory services to individuals, businesses, and institutions, focusing on long-term relationships and financial stability. This case study, which shows how Zabbix helped SEB solve its log monitoring challenges, discusses aspects specific to SEB’s operations in the Baltics, where distinct systems and structures are in place but are aligned with the group’s overall approach.

The challenge

Between 2016 and 2020, SEB launched a unified IT platform for all three Baltic countries. They encountered a wide variety of challenges, including a distinct need to unify the monitoring area. Different countries had different tools and different attitudes regarding the way monitoring should operate. After numerous discussions and weighing the pros and cons of different monitoring tools, SEB concluded that the most effective way to achieve unification would be to (re)implement everything necessary with Zabbix.

It turned out that a great deal of valuable data for monitoring resides in logs. The logs varied in update frequency and structure, as did the requirements for data extraction. Some monitoring items were simple regex patterns to count matching entities or catch errors, while others had more complex logic, such as joining multiple lines for evaluation or dynamically detecting specific patterns to observe.

At the start of SEB’s journey with Zabbix, they were using version 3.0, which came with some now long-forgotten limitations:

  • No log.count[*] item yet
  • No PCRE regular expressions – only ERE was available
  • Very limited dashboard and visualization capabilities

The solution

To address all the log-related challenges, SEB chose to leverage Zabbix’s “UserParameter” capabilities. This feature is invaluable for extending Zabbix functionality.

log.discovery

This custom approach relies on the ability to effectively convert regex capturing groups into LLD (Low-Level Discovery) objects. When new elements that need monitoring appear in the logs, corresponding monitoring objects can be automatically created in Zabbix. This process was covered in more detail at Zabbix Summit 2023.

For instance, an effective set of metrics is extracted from logs to monitor the SEB mobile app. Request processing durations are logged alongside other parameters, enabling efficient grouping, such as by endpoint name and HTTP status code. This approach accommodates a wide range of potential combinations for “endpoint + HTTP status code”:

[root@linux ~]# ./log_discovery.sh "${my_log}" 1000000 COMPONENT "response\":.\"status\":(\d{3}).*uriPattern\":\"([^ ]+)\",.timing" | jq '.' | grep -c COMPONENT_1
205
[root@linux ~]#

LLD is able to gather them all:

For each discovered couple, monitoring of request processing durations is added, both for individual durations and 1 minute averages:

Certain significant combinations are enhanced with triggers, efficiently managed using the “Override” section in the LLD configuration to ensure they are created only for specific cases. So with this approach, some unexpected slowness can be nicely caught:

log.reader

For complex data collection scenarios, there was a need to implement a solution that allows data to be extracted from logs with minimal limitations. The approach was to create a log reading mechanism that could support any required data extraction logic on top of it. This was covered in more detail at Zabbix Summit 2024.

Zabbix agent 2

In addition to the mentioned custom log processing techniques, SEB had a good reason to use “Zabbix agent 2”. Both log[*] and log.count[*] are of the “Active” item type. These items are not processed in parallel by the Zabbix agent. In places with a large number of log-based items, “Zabbix Agent 2” was used, because it supports the concurrent processing of active checks.

The results

The ability to use LLD on logs was a game-changer and a lifesaver for SEB. Imagine hundreds of different items discovered from a single rule, along with the requirement to monitor any new entity matching a specific pattern as soon as it appears. Without LLD, meeting such a requirement would have been simply impossible. This approach covers many different areas, including mission-critical metrics such as counts of various requests and processing durations.

The ability to slice logs themselves and create any needed logic on top makes almost any custom log monitoring requirement possible. It gives the ability to analyze data in ways that wouldn’t be possible otherwise (e.g. average duration monitoring for large set of data).

In conclusion

SEB Bank in the Baltics relies heavily on data collection from logs. Zabbix is flexible enough to meet most of their needs when it comes to log monitoring, and – most importantly – it allows for custom implementations where required. This flexibility is highly appreciated, as it removes many barriers when monitoring the various components of SEB’s IT ecosystem and business functions.

The post Solving Log Monitoring Challenges at SEB Bank appeared first on Zabbix Blog.

Monitoring VMware vSphere with Zabbix

Post Syndicated from Mateusz Romaniuk original https://blog.zabbix.com/monitoring-vmware-vsphere-with-zabbix/29193/

Zabbix is an open-source monitoring tool designed to oversee multiple IT infrastructure components, including networks, servers, virtual machines, and cloud services. It operates using both agent-based and agentless monitoring methods. Agents can be installed on monitored devices to collect performance data and report back to a centralized Zabbix server.

Zabbix provides comprehensive integration capabilities for monitoring VMware environments, including ESXi hypervisors, vCenter servers, and virtual machines (VMs). This integration allows administrators to effectively track performance metrics and resource usage across their VMware infrastructure.

In this post, I will show you how to set up Zabbix monitoring with a VMware vSphere infrastructure.

Requirements:

  • Zabbix server
  • Access to the VMware vCenter Server

Step one: Create a Zabbix service user in the vCenter

First things first, let’s create a service user on the vCenter that will be used by the Zabbix server to collect data. To make life easier, in my lab setup the user [email protected] will have full Administrator privileges. Read-only permissions should be enough, however.

1. In the vSphere Client, choose Menu -> Administration -> Users and Groups. From the Users tab, select Domain vsphere.local, and click the ADD button to add a new user.

2. Type a username and password. Click ADD to create a new user.

3. Change the tab to Groups and select the Administrators group.

4. Find a new user zabbix, click on it and save. The user is added to the Administrators group.

5. From the Host and Clusters view, choose vCenter name and go to the Permissions tab. Click the Add button.

6. Choose a proper domain (vsphere.local), find the user zabbix, set the role to Administrator, and check Propagate to children. Click OK to give those permissions.

Step two: Make changes on the Zabbix server

Next, we need to edit zabbix_server.conf. In this file we need to enable the vmware collector process. It’s necessary to start VMware monitoring. FYI, I have installed Zabbix server in version 7.0.4.

1. Edit a configuration file zabbix_server.conf

vim /etc/zabbix/zabbix_server.conf

2. Find the StartVMwareCollectors parameter, delete “#” before it and change the value from 0 to at least 2. Save the file and exit.

Except for StartVMwareCollectors which is mandatory, it’s possible to enable and modify additional VMware parameters. You can find more details about them HERE.
VMwareCacheSize
VMwareFrequency
VMwarePerfFrequency
VMwareTimeout

3. Restart the zabbix-server service.

systemctl restart zabbix-server

Step three: Configure the VMware template on Zabbix

1.Log in to the Zabbix server via GUI – http://zabbix_server/zabbix. Go to the Hosts section under the Monitoring tab.

2. Create a new “Host.” Click Create Host in the right upper corner.

3. In the Host tab provide the following details:

Host name – type the name of the system that we want to monitor – here it is VMware Infrastructure.
Templates – type/find template name “VMware”, more info about VMware template you can find HERE.
Host groups – find/type “VMware(new)” host group.

At this point,  go to the Macros tab.

4. In the Macros tab you need to provide 3 values/macros. These macros describes data that is needed to connect Zabbix to the VMware vCenter:

{$VMWARE.URL} – VMware service (vCenter or ESXi hypervisor) SDK URL (https://servername/sdk) that we want to connect.
{$VMWARE.USERNAME} – VMware service username created in the 1 section.
{$VMWARE.PASSWORD} – VMware service user password created in the 1 section.

Click the Add button.

5. A new Host was created and data collection is in progress.

6. Depending on the size of the infrastructure, data collection takes different amounts of time. Once configured, Zabbix will automatically discover VMs and begin collecting performance data. You can find an overview of the latest data in the Dashboard screen.

7. More specific and detailed data can be found in Latest data under the Monitoring tab.

In Host groups or Hosts choose the name of the item you are looking for (you can also click the “Select” button). Select the name of the ESXi host, the virtual machine, the vCenter name, the datastore, or all VMware information.

Zabbix can collect multiple metrics from VMware using its built-in templates. These metrics include:

– CPU usage
– Memory consumption
– Disk I/O statistics
– Network traffic
– Datastore capacity

In conclusion

Integrating Zabbix with VMware provides a robust solution for monitoring virtualized environments and enhancing visibility into system performance and resource utilization, while enabling timely alerts and responses to operational issues.

The post Monitoring VMware vSphere with Zabbix appeared first on Zabbix Blog.

Texas Sues GM for Collecting Driving Data without Consent

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/08/texas-sues-gm-for-collecting-driving-data-without-consent.html

Texas is suing General Motors for collecting driver data without consent and then selling it to insurance companies:

From CNN:

In car models from 2015 and later, the Detroit-based car manufacturer allegedly used technology to “collect, record, analyze, and transmit highly detailed driving data about each time a driver used their vehicle,” according to the AG’s statement.

General Motors sold this information to several other companies, including to at least two companies for the purpose of generating “Driving Scores” about GM’s customers, the AG alleged. The suit said those two companies then sold these scores to insurance companies.

Insurance companies can use data to see how many times people exceeded a speed limit or obeyed other traffic laws. Some insurance firms ask customers if they want to voluntarily opt-in to such programs, promising lower rates for safer drivers.

But the attorney general’s office claimed GM “deceived” its Texan customers by encouraging them to enroll in programs such as OnStar Smart Driver. But by agreeing to join these programs, customers also unknowingly agreed to the collection and sale of their data, the attorney general’s office said.

Press release. Court filing. Slashdot thread.

The Hacking of Culture and the Creation of Socio-Technical Debt

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/06/the-hacking-of-culture-and-the-creation-of-socio-technical-debt.html

Culture is increasingly mediated through algorithms. These algorithms have splintered the organization of culture, a result of states and tech companies vying for influence over mass audiences. One byproduct of this splintering is a shift from imperfect but broad cultural narratives to a proliferation of niche groups, who are defined by ideology or aesthetics instead of nationality or geography. This change reflects a material shift in the relationship between collective identity and power, and illustrates how states no longer have exclusive domain over either. Today, both power and culture are increasingly corporate.

Blending Stewart Brand and Jean-Jacques Rousseau, McKenzie Wark writes in A Hacker Manifesto that “information wants to be free but is everywhere in chains.”1 Sounding simultaneously harmless and revolutionary, Wark’s assertion as part of her analysis of the role of what she terms “the hacker class” in creating new world orders points to one of the main ideas that became foundational to the reorganization of power in the era of the internet: that “information wants to be free.” This credo, itself a co-option of Brand’s influential original assertion in a conversation with Apple cofounder Steve Wozniak at the 1984 Hackers Conference and later in his 1987 book The Media Lab: Inventing the Future at MIT, became a central ethos for early internet inventors, activists,2 and entrepreneurs. Ultimately, this notion was foundational in the construction of the era we find ourselves in today: an era in which internet companies dominate public and private life. These companies used the supposed desire of information to be free as a pretext for building platforms that allowed people to connect and share content. Over time, this development helped facilitate the definitive power transfer of our time, from states to corporations.

This power transfer was enabled in part by personal data and its potential power to influence people’s behavior—a critical goal in both politics and business. The pioneers of the digital advertising industry claimed that the more data they had about people, the more they could influence their behavior. In this way, they used data as a proxy for influence, and built the business case for mass digital surveillance. The big idea was that data can accurately model, predict, and influence the behavior of everyone—from consumers to voters to criminals. In reality, the relationship between data and influence is fuzzier, since influence is hard to measure or quantify. But the idea of data as a proxy for influence is appealing precisely because data is quantifiable, whereas influence is vague. The business model of Google Ads, Facebook, Experian, and similar companies works because data is cheap to gather, and the effectiveness of the resulting influence is difficult to measure. The credo was “Build the platform, harvest the data…then profit.” By 2006, a major policy paper could ask, “Is Data the New Oil?”3

The digital platforms that have succeeded most in attracting and sustaining mass attention—Facebook, TikTok, Instagram—have become cultural. The design of these platforms dictates the circulation of customs, symbols, stories, values, and norms that bind people together in protocols of shared identity. Culture, as articulated through human systems such as art and media, is a kind of social infrastructure. Put differently, culture is the operating system of society.

Like any well-designed operating system, culture is invisible to most people most of the time. Hidden in plain sight, we make use of it constantly without realizing it. As an operating system, culture forms the base infrastructure layer of societal interaction, facilitating communication, cooperation, and interrelations. Always evolving, culture is elastic: we build on it, remix it, and even break it.

Culture can also be hacked—subverted for specific advantage.4 If culture is like an operating system, then to hack it is to exploit the design of that system to gain unauthorized control and manipulate it towards a specific end. This can be for good or for bad. The morality of the hack depends on the intent and actions of the hacker.

When businesses hack culture to gather data, they are not necessarily destroying or burning down social fabrics and cultural infrastructure. Rather, they reroute the way information and value circulate, for the benefit of their shareholders. This isn’t new. There have been culture hacks before. For example, by lending it covert support, the CIA hacked the abstract expressionism movement to promote the idea that capitalism was friendly to high culture.5 Advertising appropriated the folk-cultural images of Santa Claus and the American cowboy to sell Coca-Cola and Marlboro cigarettes, respectively. In Mexico, after the revolution of 1910, the ruling party hacked muralist works, aiming to construct a unifying national narrative.

Culture hacks under digital capitalism are different. Whereas traditional propaganda goes in one direction—from government to population, or from corporation to customers—the internet-surveillance business works in two directions: extracting data while pushing engaging content. The extracted data is used to determine what content a user would find most engaging, and that engagement is used to extract more data, and so on. The goal is to keep as many users as possible on platforms for as long as possible, in order to sell access to those users to advertisers. Another difference between traditional propaganda and digital platforms is that the former aims to craft messages with broad appeal, while the latter hyper-personalizes content for individual users.

The rise of Chinese-owned TikTok has triggered heated debate in the US about the potential for a foreign-owned platform to influence users by manipulating what they see. Never mind that US corporations have used similar tactics for years. While the political commitments of platform owners are indeed consequential—Chinese-owned companies are in service to the Chinese Communist Party, while US-owned companies are in service to business goals—the far more pressing issue is that both have virtually unchecked surveillance power. They are both reshaping societies by hacking culture to extract data and serve content. Funny memes, shocking news, and aspirational images all function similarly: they provide companies with unprecedented access to societies’ collective dreams and fears.6 By determining who sees what when and where, platform owners influence how societies articulate their understanding of themselves.

Tech companies want us to believe that algorithmically determined content is effectively neutral: that it merely reflects the user’s behavior and tastes back at them. In 2021, Instagram head Adam Mosseri wrote a post on the company’s blog entitled “Shedding More Light on How Instagram Works.” A similar window into TikTok’s functioning was provided by journalist Ben Smith in his article “How TikTok Reads Your Mind.”7 Both pieces boil down to roughly the same idea: “We use complicated math to give you more of what your behavior shows us you really like.”

This has two consequences. First, companies that control what users see in a nontransparent way influence how we perceive the world. They can even shape our personal relationships. Second, by optimizing algorithms for individual attention, a sense of culture as common ground is lost. Rather than binding people through shared narratives, digital platforms fracture common cultural norms into self-reinforcing filter bubbles.8

This fragmentation of shared cultural identity reflects how the data surveillance business is rewriting both the established order of global power, and social contracts between national governments and their citizens. Before the internet, in the era of the modern state, imperfect but broad narratives shaped distinct cultural identities; “Mexican culture” was different from “French culture,” and so on. These narratives were designed to carve away an “us” from “them,” in a way that served government aims. Culture has long been understood to operate within the envelope of nationality, as exemplified by the organization of museum collections according to the nationality of artists, or by the Venice Biennale—the Olympics of the art world, with its national pavilions format.

National culture, however, is about more than museum collections or promoting tourism. It broadly legitimizes state power by emotionally binding citizens to a self-understood identity. This identity helps ensure a continuing supply of military recruits to fight for the preservation of the state. Sociologist James Davison Hunter, who popularized the phrase “culture war,” stresses that culture is used to justify violence to defend these identities.9 We saw an example of this on January 6, 2021, with the storming of the US Capitol. Many of those involved were motivated by a desire to defend a certain idea of cultural identity they believed was under threat.

Military priorities were also entangled with the origins of the tech industry. The US Department of Defense funded ARPANET, the first version of the internet. But the internet wouldn’t have become what it is today without the influence of both West Coast counterculture and small-l libertarianism, which saw the early internet as primarily a space to connect and play. One of the first digital game designers was Bernie De Koven, founder of the Games Preserve Foundation. A noted game theorist, he was inspired by Stewart Brand’s interest in “play-ins” to start a center dedicated to play. Brand had envisioned play-ins as an alternative form of protest against the Vietnam War; they would be their own “soft war” of subversion against the military.10 But the rise of digital surveillance as the business model of nascent tech corporations would hack this anti-establishment spirit, turning instruments of social cohesion and connection into instruments of control.

It’s this counterculture side of tech’s lineage, which advocated for the social value of play, that attuned the tech industry to the utility of culture. We see the commingling of play and military control in Brand’s Whole Earth Catalog, which was a huge influence on early tech culture. Described as “a kind of Bible for counterculture technology,” the Whole Earth Catalog was popular with the first generation of internet engineers, and established crucial “assumptions about the ideal relationships between information, technology, and community.”11 Brand’s 1972 Rolling Stone article “Spacewar: Fantastic Life and Symbolic Death Among the Computer” further emphasized how rudimentary video games were central to the engineering community. These games were wildly popular at leading engineering research centers: Stanford, MIT, ARPA, Xerox, and others. This passion for gaming as an expression of technical skills and a way for hacker communities to bond led to the development of MUD (Multi-User Dungeon) programs, which enabled multiple people to communicate and collaborate online simultaneously.

The first MUD was developed in 1978 by engineers who wanted to play fantasy games online. It applied the early-internet ethos of decentralism and personalization to video games, making it a precursor to massive multiplayer online role-playing games and modern chat rooms and Facebook groups. Today, these video games and game-like simulations—now a commercial industry worth around $200 billion12—serve as important recruitment and training tools for the military.13 The history of the tech industry and culture is full of this tension between the internet as an engineering plaything and as a surveillance commodity.

Historically, infrastructure businesses—like railroad companies in the nineteenth-century US—have always wielded considerable power. Internet companies that are also infrastructure businesses combine commercial interests with influence over national and individual security. As we transitioned from railroad tycoons connecting physical space to cloud computing companies connecting digital space, the pace of technological development put governments at a disadvantage. The result is that corporations now lead the development of new tech (a reversal from the ARPANET days), and governments follow, struggling to modernize public services in line with the new tech. Companies like Microsoft are functionally providing national cybersecurity. Starlink, Elon Musk’s satellite internet service, is a consumer product that facilitates military communications for the war in Ukraine. Traditionally, this kind of service had been restricted to selected users and was the purview of states.14 Increasingly, it is clear that a handful of transnational companies are using their technological advantages to consolidate economic and political power to a degree previously afforded to only great-power nations.

Worse, since these companies operate across multiple countries and regions, there is no regulatory body with the jurisdiction to effectively constrain them. This transition of authority from states to corporations and the nature of surveillance as the business model of the internet rewrites social contracts between national governments and their citizens. But it also also blurs the lines among citizen, consumer, and worker. An example of this are Google’s Recaptchas, visual image puzzles used in cybersecurity to “prove” that the user is a human and not a bot. While these puzzles are used by companies and governments to add a layer of security to their sites, their value is in how they record a user’s input in solving the puzzles to train Google’s computer vision AI systems. Similarly, Microsoft provides significant cybersecurity services to governments while it also trains its AI models on citizens’ conversations with Bing.15 Under this dyanmic, when citizens use digital tools and services provided by tech companies, often to access government webpages and resources, they become de facto free labor for the tech companies providing them. The value generated by this citizen-user-laborer stays with the company, as it is used to develop and refine their products. In this new blurred reality, the relationships among corporations, governments, power, and identity are shifting. Our social and cultural infrastructure suffers as a result, creating a new kind of technical debt of social and cultural infrustructure.

In the field of software development, technical debt refers to the future cost of ignoring a near-term engineering problem.16 Technical debt grows as engineers implement short-term patches or workarounds, choosing to push the more expensive and involved re-engineering fixes for later. This debt accrues over time, to be paid back in the long term. The result of a decision to solve an immediate problem at the expense of the long-term one effectively mortgages the future in favor of an easier present. In terms of cultural and social infrastructure, we use the same phrase to refer to the long-term costs that result from avoiding or not fully addressing social needs in the present. More than a mere mistake, socio-technical debt stems from willfully not addressing a social problem today and leaving a much larger problem to be addressed in the future.

For example, this kind of technical debt was created by the cratering of the news industry, which relied on social media to drive traffic—and revenue—to news websites. When social media companies adjusted their algorithms to deprioritize news, traffic to news sites plummeted, causing an existential crisis for many publications.17 Now, traditional news stories make up only 3 percent of social media content. At the same time, 66 percent of people ages eighteen to twenty-four say they get their “news” from TikTok, Facebook, and Twitter.18 To be clear, Facebook did not accrue technical debt when it swallowed the news industry. We as a society are dealing with technical debt in the sense that we are being forced to pay the social cost of allowing them to do that.

One result of this shift in information consumption as a result of changes to the cultural infrastructure of social media is the rise in polarization and radicalism. So by neglecting to adequately regulate tech companies and support news outlets in the near term, our governments have paved the way for social instability in the long term. We as a society also have to find and fund new systems to act as a watchdog over both corporate and governmental power.

Another example of socio-technical debt is the slow erosion of main streets and malls by e-commerce.19 These places used to be important sites for physical gathering, which helped the shops and restaurants concentrated there stay in business. But e-commerce and direct-to-consumer trends have undermined the economic viability of main streets and malls, and have made it much harder for small businesses to survive. The long-term consequence of this to society is the hollowing out of town centers and the loss of spaces for physical gathering—which we will all have to pay for eventually.

The faltering finances of museums will also create long-term consequences for society as a whole, especially in the US, where Museums mostly depend on private donors to cover operational costs. But a younger generation of philanthropists is shifting its giving priorities away from the arts, leading to a funding crisis at some institutions.20

One final example: libraries. NYU Sociologist Eric Klinenberg called libraries “the textbook example of social infrastructure in action.”21 But today they are stretched to the breaking point, like museums, main streets, and news media. In New York City, Mayor Eric Adams has proposed a series of severe budget cuts to the city’s library system over the past year, despite having seen a spike in usage recently. The steepest cuts were eventually retracted, but most libraries in the city have still had to cancel social programs and cut the number of days they’re open.22 As more and more spaces for meeting in real life close, we increasingly turn to digital platforms for connection to replace them. But these virtual spaces are optimized for shareholder returns, not public good.

Just seven companies—Alphabet (the parent company of Google), Amazon, Apple, Meta, Microsoft, Nvidia and Tesla—drove 60 percent of the gains of the S&P stock market index in 2023.23 Four—Alibaba, Amazon, Google, and Microsoft—deliver the majority of cloud services.24 These companies have captured the delivery of digital and physical goods and services. Everything involved with social media, cloud computing, groceries, and medicine is trapped in their flywheels, because the constellation of systems that previously put the brakes on corporate power, such as monopoly laws, labor unions, and news media, has been eroded. Product dependence and regulatory capture have further undermined the capacity of states to respond to the rise in corporate hard and soft power. Lock-in and other anticompetitive corporate behavior have prevented market mechanisms from working properly. As democracy falls into deeper crisis with each passing year, policy and culture are increasingly bent towards serving corporate interest. The illusion that business, government, and culture are siloed sustains this status quo.

Our digitized global economy has made us all participants in the international data trade, however reluctantly. Though we are aware of the privacy invasions and social costs of digital platforms, we nevertheless participate in these systems because we feel as though we have no alternative—which itself is partly the result of tech monopolies and the lack of competition.

Now, the ascendence of AI is thrusting big data into a new phase and new conflicts with social contracts. The development of bigger, more powerful AI models means more demand for data. Again, massive wholesale extractions of culture are at the heart of these efforts.25 As AI researchers and artists Kate Crawford and Vladan Joler explain in the catalog to their exhibition Calculating Empires, AI developers require “the entire history of human knowledge and culture … The current lawsuits over generative systems like GPT and Stable Diffusion highlight how completely dependent AI systems are on extracting, enclosing, and commodifying the entire history of cognitive and creative labor.”26

Permitting internet companies to hack the systems in which culture is produced and circulates is a short-term trade-off that has proven to have devastating long-term consequences. When governments give tech companies unregulated access to our social and cultural infrastructure, the social contract becomes biased towards their profit. When we get immediate catharsis through sharing memes or engaging in internet flamewars, real protest is muzzled. We are increasing our collective socio-technical debt by ceding our social and cultural infrastructure to tech monopolies.

Cultural expression is fundamental to what makes us human. It’s an impulse, innate to us as a species, and this impulse will continue to be a gold mine to tech companies. There is evidence that AI models trained on synthetic data—data produced by other AI models rather than humans—can corrupt these models, causing them to return false or nonsensical answers to queries.27 So as AI-produced data floods the internet, data that is guaranteed to have been derived from humans becomes more valuable. In this context, our human nature, compelling us to make and express culture, is the dream of digital capitalism. We become a perpetual motion machine churning out free data. Beholden to shareholders, these corporations see it as their fiduciary duty—a moral imperative even—to extract value from this cultural life.

We are in a strange transition. The previous global order, in which states wielded ultimate authority, hasn’t quite died. At the same time, large corporations have stepped in to deliver some of the services abandoned by states, but at the price of privacy and civic well-being. Increasingly, corporations provide consistent, if not pleasant, economic and social organization. Something similar occurred during the Gilded Age in the US (1870s–1890s). But back then, the influence of robber barons was largely constrained to the geographies in which they operated, and their services (like the railroad) were not previously provided by states. In our current transitionary period, public life worldwide is being reimagined in accordance with corporate values. Amidst a tug-of-war between the old state-centric world and the emerging capital-centric world, there is a growing radicalism fueled partly by frustration over social and personal needs going unmet under a transnational order that is maximized for profit rather than public good.

Culture is increasingly divorced from national identity in our globalized, fragmented world. On the positive side, this decoupling can make culture more inclusive of marginalized people. Other groups, however, may perceive this new status quo as a threat, especially those facing a loss of privilege. The rise of white Christian nationalism shows that the right still regards national identity and culture as crucial—as potent tools in the struggle to build political power, often through anti-democratic means. This phenomenon shows that the separation of cultural identity from national identity doesn’t negate the latter. Instead, it creates new political realities and new orders of power.

Nations issuing passports still behave as though they are the definitive arbiters of identity. But culture today—particularly the multiverse of internet cultures—exposes how this is increasingly untrue. With government discredited as an ultimate authority, and identity less and less connected to nationality, we can find a measure of hope for navigating the current transition in the fact that culture is never static. New forms of resistance are always emerging. But we must ask ourselves: Have the tech industry’s overwhelming surveillance powers rendered subversion impossible? Or does its scramble to gather all the world’s data offer new possibilities to hack the system?

 

1. McKenzie Wark, A Hacker Manifesto (Harvard University Press, 2004), thesis 126.

2. Jon Katz, “Birth of a Digital Nation,” Wired, April 1, 1997.

3. Marcin Szczepanski, “Is Data the New Oil? Competition Issues in the Digital Economy,” European Parliamentary Research Service, January 2020.

4. Bruce Schneier, A Hacker’s Mind: How the Powerful Bend Society’s Rules, and How to Bend Them Back (W. W. Norton & Sons, 2023).

5. Lucie Levine, “Was Modern Art Really a CIA Psy-Op?” JStor Daily, April 1, 2020.

6. Bruce Schneier, Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World (W. W. Norton & Sons, 2015).

7. Adam Mosseri, “Shedding More Light on How Instagram Works,” Instagram Blog, June 8, 2021; Ben Smith, “How TikTok Reads Your Mind,” New York Times, December 5, 2021.

8. Giacomo Figà Talamanca and Selene Arfini, “Through the Newsfeed Glass: Rethinking Filter Bubbles and Echo Chambers,” Philosophy & Technology 35, no. 1 (2022).

9. Zack Stanton, “How the ‘Culture War’ Could Break Democracy,” Politico, May 5, 2021.

10. Jason Johnson, “Inside the Failed, Utopian New Games Movement,” Kill Screen, October 25, 2013.

11. Fred Turner, “Taking the Whole Earth Digital,” chap. 4 in From Counter Culture to Cyberculture: Stewart Brand, The Whole Earth Network, and the Rise of Digital Utopianism (University of Chicago Press, 2006).

12. Kaare Ericksen, “The State of the Video Games Industry: A Special Report,” Variety, February 1, 2024.

13. Rosa Schwartzburg, “The US Military Is Embedded in the Gaming World. It’s Target: Teen Recruits,” The Guardian, February 14, 2024; Scott Kuhn, “Soldiers Maintain Readiness Playing Video Games,” US Army, April 29, 2020; Katie Lange, “Military Esports: How Gaming Is Changing Recruitment & Moral,” US Department of Defense, December 13, 2022.

14. Shaun Waterman, “Growing Commercial SATCOM Raises Trust Issues for Pentagon,” Air & Space Forces Magazine, April 3, 2024.

15. Geoffrey A Fowler, “Your Instagrams Are Training AI. There’s Little You Can Do About It,” Washington Post, September 27, 2023.

16. Zengyang Li, Paris Avgeriou, and Peng Liang, “A Systematic Mapping Study on Technical Debt and Its Management,” Journal of Systems and Software, December 2014.

17. David Streitfeld, “How the Media Industry Keeps Losing the Future,” New York Times, February 28, 2024.

18. “The End of the Social Network,” The Economist, February 1, 2024; Ollie Davies, “What Happens If Teens Get Their News From TikTok?” The Guardian, February 22, 2023.

19. Eric Jaffe, “Quantifying the Death of the Classic American Main Street,” Medium, March 16, 2018.

20. Julia Halprin, “The Hangover from the Museum Party: Institutions in the US Are Facing a Funding Crisis,” Art Newspaper, January 19, 2024.

21. Quoted in Pete Buttigieg, “The Key to Happiness Might Be as Simple as a Library or Park,” New York Times, September 14, 2018.

22. Jeffery C. Mays and Dana Rubinstein, “Mayor Adams Walks Back Budget Cuts Many Saw as Unnecessary,” New York Times, April 24, 2024.

23. Karl Russell and Joe Rennison, “These Seven Tech Stocks Are Driving the Market,” New York Times, January 22, 2024.

24. Ian Bremmer, “How Big Tech Will Reshape the Global Order,” Foreign Affairs, October 19, 2021.

25. Nathan Sanders and Bruce Schneier, “How the ‘Frontier’ Became the Slogan for Uncontrolled AI,” Jacobin, February 27, 2024.

26. Kate Crawford and Vladan Joler, Calculating Empires: A Genealogy of Technology and Power, 1500–2025 (Fondazione Prada, 2023), 9. Exhibition catalog.

27. Rahul Rao, “AI Generated Data Can Poison Future AI Models,” Scientific American, July 28, 2023.

This essay was written with Kim Córdova, and was originally published in e-flux.

Case Study: Zabbix at the European Space Agency

Post Syndicated from Arturs Lontons original https://blog.zabbix.com/case-study-zabbix-at-the-european-space-agency/28024/

The European Space Agency (ESA) is a 22-member intergovernmental body devoted to space exploration. Headquartered in Paris and with a global staff of around 2,200, the ESA was founded in 1975. Its annual budget was €7.08 billion in 2023.

The challenge

The Columbus laboratory is the European module of the International Space Station and the cornerstone of Europe’s participation. Positioned on the starboard side of the Station’s leading edge, it is designed to provide an environment for pursuing research and development in a wide variety of fields. Its characteristics include:

  • Payload complement flexibility, provided by a modular design and serviced by a regular logistics, maintenance, and upgrade capability
  • A permanent crew presence for servicing payload support systems and interacting with payloads
  • A continuously available ground infrastructure for monitoring and controlling onboard activities

Columbus provides internal payload accommodation for multidisciplinary research into material science, fluid physics, and life sciences, while the External Payload Facility (EPF) hosts space science and Earth observation payloads.

Academics on Earth perform their tests on Columbus remotely – programming them and getting the results. The infrastructure required for these tests and the payloads that get sent back and forth require a flexible and dependable monitoring solution, and that’s where Zabbix enters the picture.

The solution

Zabbix proxy was deployed in the Columbus module alongside other software required for research, operations, and connectivity. The Zabbix server and frontend are deployed in the ground data center, and this is what the proxy communicates with.

In addition to proxies, we have a Zabbix sender and Agent 2 that are used on this infrastructure, which is made of VMS and containers running different kinds of services. Data is collected in a very ordinary fashion – Zabbix Agent 2 performs native checks because there is still server hardware running with operating systems and OS level resources that need to be monitored by the Agent.

We mix these native checks with user parameters which execute custom checks based on scripts or commands for commercial off-the-shelf components. The agent is extended depending on the requirements of the components. It then collects those metrics and sends them to the proxy. Scripting is used for custom components, and because Zabbix is language agnostic, any type of programming or scripting language works. It gets wrapped together with Zabbix sender, which then sends data to the proxy, which then sends data to the server.

Because there are so many custom services and metrics that need to be monitored (the number of high priority files in the transfer queue for a particular payload, for example) and because metrics, services, and payloads can change over time, the ESA needed to automate a way of automatically discovering these, displaying them, and collecting data for them. We used low-level discovery together with some scripting to discover and automatically start monitoring new payloads.

The results

Thanks to Zabbix’s visualization capabilities, the ESA’s team has found it easy to observe their dashboards and react to alerts in real time.

The ESA has also found Zabbix proxy to be an ideal solution for their needs, because if a link between the ground data center and the Columbus module goes down, the proxy keeps collecting local metrics, stores them in its own database, and then sends them back to the ground once the connection is restored. No such situation has arisen yet, but Zabbix has performed flawlessly in all test scenarios.

Monitoring is a never-ending process and there’s always room for improvement, but it’s reasonable to expect that the ESA will continue working on Zabbix and finding new metrics and new ways of improving monitoring.

In conclusion

Part of the beauty of Zabbix has always been its adaptability – it can be as simple or as complex as any user needs it to be. The simplicity of the Zabbix setup in this scenario (as opposed to a more modular setup with 4 or 5 pieces of software) is what makes it ideal for deployment by the ESA on the International Space Station.

To learn more about what we do for customers across multiple industries, visit our website or request a demo.

The post Case Study: Zabbix at the European Space Agency appeared first on Zabbix Blog.

Surveillance by the New Microsoft Outlook App

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/04/surveillance-by-the-new-microsoft-outlook-app.html

The ProtonMail people are accusing Microsoft’s new Outlook for Windows app of conducting extensive surveillance on its users. It shares data with advertisers, a lot of data:

The window informs users that Microsoft and those 801 third parties use their data for a number of purposes, including to:

  • Store and/or access information on the user’s device
  • Develop and improve products
  • Personalize ads and content
  • Measure ads and content
  • Derive audience insights
  • Obtain precise geolocation data
  • Identify users through device scanning

Commentary.

Class-Action Lawsuit against Google’s Incognito Mode

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2024/04/class-action-lawsuit-against-googles-incognito-mode.html

The lawsuit has been settled:

Google has agreed to delete “billions of data records” the company collected while users browsed the web using Incognito mode, according to documents filed in federal court in San Francisco on Monday. The agreement, part of a settlement in a class action lawsuit filed in 2020, caps off years of disclosures about Google’s practices that shed light on how much data the tech giant siphons from its users­—even when they’re in private-browsing mode.

Under the terms of the settlement, Google must further update the Incognito mode “splash page” that appears anytime you open an Incognito mode Chrome window after previously updating it in January. The Incognito splash page will explicitly state that Google collects data from third-party websites “regardless of which browsing or browser mode you use,” and stipulate that “third-party sites and apps that integrate our services may still share information with Google,” among other changes. Details about Google’s private-browsing data collection must also appear in the company’s privacy policy.

I was an expert witness for the prosecution (that’s the class, against Google). I don’t know if my declarations and deposition will become public.

Extending Zabbix: the power of scripting

Post Syndicated from Giedrius Stasiulionis original https://blog.zabbix.com/extending-zabbix-the-power-of-scripting/27401/

Scripts can extend Zabbix in various different aspects. If you know your ways around a CLI, you will be able to extend your monitoring capabilities and streamline workflows related to most Zabbix components.

What I like about Zabbix is that it is very flexible and powerful tool right out of the box. It has many different ways to collect, evaluate and visualize data, all implemented natively and ready to use.

However, in more complex environments or custom use cases, you will inevitably face situations when something can’t be collected (or displayed) in a way that you want. Luckily enough, Zabbix is flexible even here! It provides you with ways to apply your knowledge and imagination so that even most custom monitoring scenarios would be covered. Even though Zabbix is an open-source tool, in this article I will talk about extending it without changing its code, but rather by applying something on top, with the help of scripting. I will guide you through some examples, which will hopefully pique your curiosity and maybe you will find them interesting enough to experiment and create something similar for yourself.

Although first idea which comes to ones mind when talking about scripts in Zabbix is most likely data collection, it is not the only place where scripts can help. So I will divide those examples / ideas into three sub categories:

  • Data collection
  • Zabbix internals
  • Visualization

Data collection

First things first. Data collection is a starting point for any kind of monitoring. There are multiple ways how to collect data in “custom” ways, but the easiest one is to use UserParameter capabilities. Basics of it are very nicely covered by official documentation or in other sources, e.g. in this video by Dmitry Lambert, so I will skip the “Hello World” part and provide some more advanced ideas which might be useful to consider. Also, the provided examples use common scripting themes/scenarios and you can find many similar solutions in the community, so maybe this will serve better as a reminder or a showcase for someone who has never created any custom items before.

Data collection: DB checks

There is a lot of good information on how to setup DB checks for Zabbix, so this is just a reminder, that one of the ways to do it is via custom scripts. I personally have done it for various different databases: MySQL, Oracle, PostgreSQL, OpenEdge Progress. Thing is ODBC is not always a great or permitted way to go, since some security restrictions might be in place and you can’t get direct access to DB from just anywhere you want. Or you want to transform your retrieved data in a ways that are complex and could hardly be covered by preprocessing. Then you have to rely on Zabbix agent running those queries either from localhost where DB resides or from some other place which is allowed to connect to your DB. Here is an example how you can do it for PostgreSQL

#!/bin/bash

my_dir="$(dirname ${0})"
conf_file="${my_dir}/sms_queue.conf"

[[ ! -f $conf_file ]] && echo -1 && exit 1

. ${conf_file}

export PGPASSWORD="${db_pass}"

query="SELECT COUNT(*) FROM sms WHERE sms.status IN ('retriable', 'validated');"

psql -h "${db_host}" -p "${db_port}" -U "${db_user}" -d "${db}" -c "${query}" -At 2>/dev/null

[[ $? -ne 0 ]] && echo -1 && exit 1

exit 0

Now what’s left is to feed the output of this script into Zabbix via UserParameter. Similar approach can be applied to Oracle (via sqlplus) or MySQL.

Data collection: log delay statistics

I once faced a situation when some graphs which are based on log data started having gaps. It meant something was wrong either with data collection (Zabbix agent) or with data not being there at the moment of collection (so nothing to collect). Quick check suggested it was the second one, but I needed to prove it somehow.

Since these log lines had timestamps of creation, it was a logical step to try to measure, how much do they differ from “current time” of reading. And this is how I came up with the following custom script to implement such idea.

First of all, we need to read the file, say once each minute. We are talking about log with several hundreds of thousands lines per minute, so this script should be made efficient. It should read the file in portions created between two script runs. I have explained such reading in details here so now we will not focus on it.

Next what this script does is it greps timestamps only from each line and counts immediately number of unique lines with the same timestamp (degree of seconds). That is where it becomes fast – it doesn’t need to analyze each and every line individually but it can analyze already grouped content!

Finally, delay is calculated based on the difference between “now” and collected timestamps, and those counters are exactly what is then passed to Zabbix.

#!/bin/bash

my_log="${1}"

my_project="${my_log##*\/}"
my_project="${my_project%%.log}"

me="$(basename ${0})"
my_dir="/tmp/log_delays/${my_project}"

[[ ! -d ${my_dir} ]] && mkdir -p ${my_dir}

# only one instance of this script at single point of time
# this makes sure you don't damage temp files

me_running="${my_dir}/${me}.running"

# allow only one process
# but make it more sophisticated:
# script is being run each minute
# if .running file is here for more than 10 minutes, something is wrong
# delete .running and try to run once again

[[ -f $me_running && $(($(date +%s)-$(stat -c %Y $me_running))) -lt 600 ]] && exit 1

touch $me_running

[[ "${my_log}" == "" || ! -f "${my_log}" ]] && exit 1

log_read="${my_dir}/${me}.read"

# get current file size in bytes

current_size=$(wc -c < "${my_log}")

# remember how many bytes you have now for next read
# when run for first time, you don't know the previous

[[ ! -f "${log_read}" ]] && echo "${current_size}" > "${log_read}"

bytes_read=$(cat "${log_read}")
echo "${current_size}" > "${log_read}"

# if rotated, let's read from the beginning

if [[ ${bytes_read} -gt ${current_size} ]]; then
  bytes_read=0
fi



# get the portion

now=$(date +%s)

delay_1_min=0
delay_5_min=0
delay_10_min=0
delay_30_min=0
delay_45_min=0
delay_60_min=0
delay_rest=0

while read line; do

  [[ ${line} == "" ]] && continue

  line=(${line})

  ts=$(date -d "${line[1]}+00:00" +%s)

  delay=$((now-ts))

  if [[ ${delay} -lt 60 ]]; then
    delay_1_min=$((${delay_1_min}+${line[0]}))
  elif [[ ${delay} -lt 300 ]]; then
    delay_5_min=$((${delay_5_min}+${line[0]}))
  elif [[ ${delay} -lt 600 ]]; then
    delay_10_min=$((${delay_10_min}+${line[0]}))
  elif [[ ${delay} -lt 1800 ]]; then
    delay_30_min=$((${delay_30_min}+${line[0]}))
  elif [[ ${delay} -lt 2700 ]]; then
    delay_45_min=$((${delay_45_min}+${line[0]}))
  elif [[ ${delay} -lt 3600 ]]; then
    delay_60_min=$((${delay_60_min}+${line[0]}))
  else
    delay_rest=$((${delay_rest}+${line[0]}))
  fi

done <<< "$(tail -c +$((bytes_read+1)) "${my_log}" | head -c $((current_size-bytes_read)) | grep -Po "(?<=timestamp\":\")(\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2})(?=\.)" | sort | uniq -c | sort -k1nr)"

echo "delay_1_min=${delay_1_min}
delay_5_min=${delay_5_min}
delay_10_min=${delay_10_min}
delay_30_min=${delay_30_min}
delay_45_min=${delay_45_min}
delay_60_min=${delay_60_min}
delay_rest=${delay_rest}"



rm -f "${me_running}"

exit 0

Now on Zabbix side, there is an item running this script and 7 dependent items, representing the degree of delay. Since there are many logs for which this data is collected, it is all put into LLD based on contents of specific directory:

vfs.dir.get[/var/log/logs,".*log$",,,,,1000]

This LLD then provides two macros:

And item prototypes will look like:

Those dependent items have one simple preprocessing step which takes needed number out of the script output:

So the final result is the nice graph in dashboard, showing exactly when and what degree delays do appear:

So as you see, it is relatively easy to collect just about any data you wish, once you know how. As you can see from these examples, it might be something more complex but it can also be just a simple one-liner – in any case it should be obvious that possibilities are endless when talking about scripts in data collection. If something is executable from the CLI and has a valuable output, go ahead and collect it!

Zabbix internals

Another area where scripts can be really useful is adjusting how Zabbix behaves or controlling this behavior automatically. And in this case, we will employ Zabbix API, since it’s designed exactly for such or similar purposes.

Zabbix internals: automatically disabling problematic item

In our environment, we have many logs to be analyzed. And some of them sometimes go crazy – something that we intend to catch starts appearing there too often and requires attention – typically we would have to adjust the regexp, temporarily suppress some patterns and inform responsible teams about too extensive logging. If you don’t (or can’t) pay attention quick, it might kill Zabbix – history write cache starts filling up. So what we do is automatically detect such an item with most values received during some most recent short period of time and automatically disable it.

First of all there are two items – the one measuring history write cache and the other one extracting top item in the given table

[root@linux ~]# zabbix_agentd -t zabbix.db.max[history_log,30] 2>/dev/null
zabbix.db.max[history_log,30] [t|463 1997050]
[root@linux ~]#

First number here is values gathered during provided period, second one is item id. The script behind this item looks like this

[root@linux ~]# grep zabbix.db.max /etc/zabbix/zabbix_agentd.d/userparameter_mysql.conf
UserParameter=zabbix.db.max[*],HOME=/etc/zabbix mysql -BN -e "USE zabbix; SELECT count(*), itemid FROM $1 WHERE clock >= unix_timestamp(NOW() - INTERVAL $2 MINUTE) GROUP BY itemid ORDER BY count(*) DESC LIMIT 1;"
[root@linux ~]#

And now relying on the history write cache item values showing us drop, we construct a trigger:

And as a last step, such trigger invokes action, which is running the script that disables the item with given ID with the help of Zabbix API, method “item.update”

Now we are able to avoid unexpected behavior of our data sources affecting Zabbix performance, all done automatically – thanks to the scripts!

Zabbix internals: add host to group via frontend scripts

Zabbix maintenance mode is a great feature allowing us to reduce noise or avoid some false positive alerts once specific host is known to have issues. At some point we found it would be convenient to be able to add (or remove) specific host into (from) maintenance directly from “Problems” window. And that is possible and achieved via a frontend script, again with the help of Zabbix API, this time methods “host.get”, “hostgroup.get”, “hostgroup.massadd” and “hostgroup.massremove”

Data visualization

Zabbix has many different widgets that are able to cover various different ways of displaying your collected data. But in some cases, you might find yourself missing some small type of “something” which would allow your dashboards to shine even more – at least I constantly face it. Starting From version 6.4 Zabbix allows you to create your own widgets but it might be not such a straightforward procedure if you have little or no programming experience. However, you can employ two already existing widgets in order to customize your dashboard look in pretty easy way.

Data visualization: URL widget

First one example is done using the URL widget. You might feed just about any content there, so if you have any web development skills, you can easily create something which would look like custom widget. Here is an example. I need a clock but not the one already provided by Zabbix as a separate clock widget – I want to have a digital clock and I also want this clock to have a section, which would display the employee on duty now and in an upcoming shift. So with a little bit of HTML, CSS and JavaScript / AJAX, I have this

With styles properly chosen, such content can be smoothly integrated into dashboards, along with other widgets.

Data visualization: plain text widget with HTML formatting

Another useful widget which is often overlooked is the “Plain text” widget – in combination with the following parameters:

It becomes a very powerful tool to display nicely formatted data snapshots. Simple yet very good example here would be to display some content, which requires human readable structure – a table.

So again, integration with other dashboard widgets is so smooth – with just some custom HTML / CSS around your data you wrap it into something that looks like brand new “table” widget. Isn’t it awesome? And you are of course not limited to tables… Just use your imagination!

Conclusion

Although I personally prefer bash as the first option to solve things, there is no big difference regarding which scripting or programming languages to choose when extending Zabbix in these ways. Just try anything you feel most comfortable with.

I hope that examples shown here inspired you in some ways. Happy scripting!

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