This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
What’s the problem with Black Friday traffic? But that’s difficult when Black Friday traffic brings overwhelming and unpredictable peak loads to retailer websites and exposes the weakest points in a company’s infrastructure, threatening application performance and user experience. Why Black Friday traffic threatens customer experience.
Migrating Critical Traffic At Scale with No Downtime — Part 1 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Hundreds of millions of customers tune into Netflix every day, expecting an uninterrupted and immersive streaming experience. This approach has a handful of benefits.
It should also be possible to analyze data in context to proactively address events, optimize performance, and remediate issues in real time. This enables proactive changes such as resource autoscaling, traffic shifting, or preventative rollbacks of bad code deployment ahead of time.
For example, if you’re monitoring network traffic and the average over the past 7 days is 500 Mbps, the threshold will adapt to this baseline. An anomaly will be identified if traffic suddenly drops below 200 Mbps or above 800 Mbps, helping you identify unusual spikes or drops.
The first part of this blog post briefly explores the integration of SLO events with AI. Consequently, the AI is founded upon the related events, and due to the detection parameters (threshold, period, analysis interval, frequent detection, etc), an issue arose. By analogy, envision an apple tree where an apple drops.
Part 3: System Strategies and Architecture By: VarunKhaitan With special thanks to my stunning colleagues: Mallika Rao , Esmir Mesic , HugoMarques This blog post is a continuation of Part 2 , where we cleared the ambiguity around title launch observability at Netflix. We call this capability TimeTravel.
Using the source of truth: Logs serve as a reliable source of truth by providing a comprehensive record of system events. To detect issues proactively, we need to simulate traffic and predict system behavior in advance. Once artificial traffic is generated, discarding the response object and relying solely on logs becomes inefficient.
We can experiment with different content placements or promotional strategies to boost visibility and engagement. Analyzing impression history, for example, might help determine how well a specific row on the home page is functioning or assess the effectiveness of a merchandising strategy.
Scaling RabbitMQ ensures your system can handle growing traffic and maintain high performance. Youll also learn strategies for maintaining data safety and managing node failures so your RabbitMQ setup is always up to the task. Think of it as a sophisticated traffic management system that ensures smooth flow and prevents congestion.
To do this, we devised a novel way to simulate the projected traffic weeks ahead of launch by building upon the traffic migration framework described here. New content or national events may drive brief spikes, but, by and large, traffic is usually smoothly increasing or decreasing.
The three strategies we will discuss today are AB Testing , Replay Testing, and Sticky Canaries. Let’s discuss the three testing strategies in further detail. The control group’s traffic utilized the legacy Falcor stack, while the experiment population leveraged the new GraphQL client and was directed to the GraphQL Shim.
Even when the staging environment closely mirrors the production environment, achieving a complete replication of all potential scenarios, such as simulating extremely high traffic volumes to assess software performance, remains challenging. This can lead to a lack of insight into how the code will behave when exposed to heavy traffic.
As recent events have demonstrated, major software outages are an ever-present threat in our increasingly digital world. Possible scenarios A Distributed Denial of Service (DDoS) attack overwhelms servers with traffic, making a website or service unavailable.
While most government agencies and commercial enterprises have digital services in place, the current volume of usage — including traffic to critical employment, health and retail/eCommerce services — has reached levels that many organizations have never seen before or tested against. There are proven strategies for handling this.
RabbitMQ is designed for flexible routing and message reliability, while Kafka handles high-throughput event streaming and real-time data processing. Kafka is optimized for high-throughput event streaming , excelling in real-time analytics and large-scale data ingestion. What is Apache Kafka?
To address potentially high numbers of requests during online shopping events like Singles Day or Black Friday, it’s crucial that this online shop have a memory storage strategy that allows for speed, scaling, and resilience of all microservices, especially the shopping cart service.
In my last blog , I’ve provided an example of this happening, whereby the traffic spiked and quadrupled the usual incoming traffic. These are all interesting metrics from marketing point of view, and also highly interesting to you as they allow you to engage with the teams that are driving the traffic against your IT-system.
This means that Dynatrace alerts more quickly when an error spike occurs in a high-traffic service (compared to a low-traffic service where statistical confidence is lower). This setting distinguishes between slowdown alerts and error detection, so that you can choose an individual strategy for each.
Building on these foundational abstractions, we developed the TimeSeries Abstraction — a versatile and scalable solution designed to efficiently store and query large volumes of temporal event data with low millisecond latencies, all in a cost-effective manner across various use cases. Let’s dive into the various aspects of this abstraction.
Network traffic growth is the main reason for increasing spending, largely because of the adoption of hybrid and multi-cloud architectures. Unifying data, unifying observability Merging siloed data streams in a unified observ ability strategy requires a different approach.
Organizations that have transitioned to agile software development strategies (including the adoption of a DevOps culture and continuous delivery automation) enforce automated solutions for such decision making—or at the very least, use automation in the gathering of a release-quality metrics. Events ingestion. Kubernetes metadata.
Let’s delve deeper into how these capabilities can transform your observability strategy, starting with our new syslog support. It also enhances syslog messages with additional context and optimizes network traffic, improving overall system resilience and security.
IT teams spend months preparing for the peak traffic they anticipate will arrive with holiday shopping. Decentralized last-mile delivery strategies such as micro-fulfillment centers complicate inventory management and order fulfillment oversight. (Though the three-second rule for page load time is often misinterpreted).
For example, an organization might use security analytics tools to monitor user behavior and network traffic. Improved compliance A better understanding of data security across multiple applications and environments provides a unified view of events and information. This offers two advantages for compliance.
During this event, we generate a timestamp and store it in an eBPF hash map using the process ID as the key. Each event includes a run queue latency sample with a cgroup ID, which we associate with running containers on the host. This spike would be noticeable in the userspace application if it were serving HTTP traffic.
In databases like MySQL and PostgreSQL, transaction logs are the source of CDC events. Some of DBLog’s features are: Processes captured log events in-order. Interleaves log with dump events, by taking dumps in chunks. No locks on tables are ever acquired, which prevent impacting write traffic on the source database.
With traffic growth, a single leader node handling all request volume started becoming overloaded. The path over which data travels from Titus Job Coordinator to a Titus Gateway cache can be described as a sequence of event queues with different processing speeds: A message generated by the event source may be buffered at any stage.
Data collected on page load events, for example, can include navigation start (when performance begins to be measured), request start (right before the user makes a request from the server), and speed index metrics (measure page load speed). RUM, however, has some limitations, including the following: RUM requires traffic to be useful.
The good news: even for latecomers to the compliance party, compliance is perfectly doable within the timeframe given the right tools and strategies. Many organisations adopt an observability solution to analyse the significance of events to their operations, making it ideally suited to APRA CPS 230 compliance.
Streamline development and delivery processes Nowadays, digital transformation strategies are executed by almost every organization across all industries. Whether triggered by a test result or a new release deployment, detected events work as a trigger to check the defined objectives and derive an overall status automatically.
BT has modernized with AIOps at the center of its digital transformation strategy. For BT, AIOps and digital transformation are at the center of the company’s upcoming three-to-five-year digital transformation strategy. As Baldry noted, Davis not only identifies issues but can trigger a chain of events to aid with auto-remediation.
Not only that, teams struggle to correlate events and alerts from a wide range of security tools, need to put them into context, and infer their risk for the business. Falco is an open-source, cloud-native security tool that utilizes the Linux kernel technology eBPF , to generate fine-grained networking, security, and observability events.
Implementation We decided to implement the strategy through Linux cgroups since they are fully supported by CFS, by modifying each container’s cpuset cgroup based on the desired mapping of containers to hyper-threads. We also want to leverage kernel PMC events to more directly optimize for minimal cache noise.
In databases like MySQL and PostgreSQL, transaction logs are the source of CDC events. Some of DBLog’s features are: Processes captured log events in-order. Interleaves log with dump events, by taking dumps in chunks. No locks on tables are ever acquired, which prevent impacting write traffic on the source database.
The key components of automatic failover include the primary server for write operations, standby servers for backup, and a monitor node for health checks and coordination of failover events. Automatic failover is a critical strategy to achieve this. In the event of a failure, it informs Pacemaker.
In the event of an isolated failure we first pre-scale microservices in the healthy regions after which we can shift traffic away from the failing one. In 2013 we first developed our multi-regional availability strategy in response to a catalyst that led us to re-architect the way our service operates.
In this post, we’ll walk you through the best way to host MongoDB on DigitalOcean, including the best instance types to use, disk types, replication strategy, and managed service providers. MongoDB Replication Strategies. DigitalOcean Advantages for MongoDB. The Best Way to Host #MongoDB on DigitalOcean Click To Tweet.
Change Data Capture(CDC) source connector reads from studio applications’ database transaction logs and emits the change events. The CDC events are passed on to the Data Mesh enrichment processor, which issues GraphQL queries to Studio Edge to enrich the data. CDC events can also be sent to Data Mesh via a Java Client Producer Library.
Log auditing is a cybersecurity practice that involves examining logs generated by various applications, computer systems, and network devices to identify and analyze security-related events. Log auditing is a crucial part of building a comprehensive security program.
Deployment Strategies We are all familiar with the advantages of releasing frequently and in smaller chunks. The challenge for clients is that each instance of the application runs on a Netflix member’s device and signals are derived from a firehose of events being sent by devices across the globe. which is not known in advance.
A blue-green deployment model is a software delivery release strategy based on maintaining two separate application environments. As part of testing and validation of the new version of the software, application traffic is gradually re-routed to the green environment. Why Is Blue-Green Deployment Useful?
Revised cleanup strategy for Elasticsearch snapshots. To address this storage issue for older snapshots, we’ve adapted our cleanup strategy so that monthly snapshots are now removed automatically after 6 months. Event Log now contains successful login entries when users logged in via SSO. Also in this release.
HashMap<String, SortedMap<Bytes, Bytes>> For complex data models such as structured Records or time-ordered Events, this two-level approach handles hierarchical structures effectively, allowing related data to be retrieved together. This model supports both simple and complex data models, balancing flexibility and efficiency.
Encryption Strategies for RabbitMQ RabbitMQ implements transport-level security using TLS/SSL encryption to safeguard data during transmission. When persistent messages in RabbitMQ are encrypted, it ensures that even in the event of unsanctioned access to storage hardware, confidential information stays protected and secure.
We organize all of the trending information in your field so you don't have to. Join 5,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content