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Introducing the My Caddie Golf Platform featuring Birchwood Golf Club
Introducing the My Caddie Golf Platform featuring Birchwood Golf Club
Hi there,
I hope you're well, I wanted to reach out because Your Telecoms Consultant has been recommended to us, and we have a unique opportunity that you may be interested in.
The My Caddie Golf Platform featuring Birchwood Golf Club can help you and the team generate business from our members and visitors, we are looking for a local Telecommunication Company to become our official partner.
Aligning your business with such a prestigious establishment can elevate your brand image and generate a positive association in the minds of potential customers.
This partnership presents an ideal opportunity to put your company in front of a vast, local and affluent audience whilst also giving you complimentary golf to use as you see fit. Even if you're too busy at the moment, we still like to have you on board as a trusted local company, who could provide our other partners with advice and pass on referrals.
Here are some of the features you will receive in the partnership:
- Exclusivity for your sector.
- Providing you with exposure on the members and visitors iPhone app.
- Exposure on the members and visitors Android app.
- Your branding on the flyovers on one of the holes on our Birchwood Golf Club web flyovers which is trackable and targeted to your demographic within the local area.
- Access to our networking groups between all partners and plus ones.
- Complimentary golf for you to entertain clients, colleagues and guests.
The cost is the equivalent of just £26 per week for a 2-year partnership + £399 Artwork (one-off, optional) + VAT.
Artwork is optional but if you want us to do it for you, you can change it up to 8 times over the 2 years so every quarter you can revamp it and put new offers on. We'll also give It to you for further marketing.
I have reached out to a number of companies locally and will be operating on a first come first serve basis so if the above is of interest please let me know as soon as possible to avoid disappointment.
Best wishes,
Jack StevensAccount Manager0113 5197 994
We have sent this email to info@learn.odoo.com having found your company contact details online. If you don't want to get any more emails from us you can stop them here.
West 1 Group UK Limited, registered in England and Wales under company number 07574948. Our registered office is Unit 1 Airport West, Lancaster Way, Yeadon, Leeds, West Yorkshire, LS19 7ZA.
Disclaimer: Our app operates independently. While we provide authentic and accurate hole-by-hole guides, we do not have a direct association with Birchwood Golf Club or claim any endorsement from them. We aim to offer golfers a reliable guide as they navigate their favourite courses. As a value-add for our advertisers, we offer free tee times at Birchwood Golf Club which we procure as any customer would, directly from the venue. We also host networking events, which may be held a various local venues as well as online sessions.Furthermore, advertisers have the unique opportunity to be featured in our flyovers of each golf hole. All offerings are subject to availability and terms.
by "Jack Stevens" <jack@w1g.biz> - 10:24 - 14 Feb 2025 -
What’s the future of the office?
On McKinsey Perspectives
by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:27 - 14 Feb 2025 -
Join us today for the latest announcements and brand-new product demos.
Today,
SAP announces a bold new vision.Are you ready for the next era of enterprise management?
Today at SAP Business Unleashed, SAP leaders, customers, and industry experts will come together to showcase how SAP combines AI, data, and applications like never before to unleash your full potential and make you unstoppable.
What are you waiting for? Register today to unlock your virtual front row seat to hot-off-the press announcements, brand-new product demos, high-value customer stories, and expert Q&A.
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by "SAP Business Unleashed" <sap@mailsap.com> - 10:13 - 13 Feb 2025 -
Enhance Your Cutting Precision with Our Diamond Tools!
Hi info,
I hope this email finds you well. I am Terry Tong, Sales Manager at XIAMEN AME Laser Diamond Tools Co., Ltd. With years of expertise in the construction industry, we specialize in high-performance diamond tools for concrete cutting, asphalt cutting, and stone cutting.
Our top-selling products, including the Ring Saw and Laser Arix Wall Blade, are known for their precision, durability, and cost-effectiveness. We are confident that we can provide you with the best quality at competitive prices, helping you achieve superior results in all your projects.
Explore our offerings and let's discuss how we can be your reliable partner in China!
Looking forward to your inquiry.
Best regards,
Terry Tong
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by "Achi Kimron" <achikimron6@gmail.com> - 09:19 - 13 Feb 2025 -
hello manager
Estimado gerente,
¿Cómo está? Le deseo un feliz año nuevo. Soy Lily de MSK (Tianjin) Cutting Technology Co., Ltd. Obtuve su información de contacto de Google. Somos un proveedor de herramientas de corte de carburo, fresas, taladros, machos de roscar y mandriles. También representamos la gama completa de productos Mitsubishi y VERTEX. Nuestra empresa obtuvo la certificación Rhine ISO 9001 en 2016. Durante este período, hemos seguido creciendo y desarrollándonos. Nuestros productos son de alta calidad y precio competitivo, y son bien recibidos por los clientes en muchos países. Al mismo tiempo, también ofrecemos servicios OEM gratuitos, que pueden ayudarlo a promover su marca a nivel local. Después de recibir su respuesta, le enviaré nuestro catálogo de productos.
Esperando su respuesta
Le deseo a usted y a su familia un feliz año nuevo, que todo sea como lo desea, buena salud y negocios prósperos.
by lilymsktools@mail-serve-hg.com - 01:37 - 13 Feb 2025 -
Non-Functional Requirements: The Backbone of Great Software - Part 1
Non-Functional Requirements: The Backbone of Great Software - Part 1
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Non-functional requirements (NFRs) are as critical as functional requirements because they define a system's qualities and operational parameters.
While functional requirements specify what a software product should do (for example, “users must be able to log in”), non-functional requirements define how well it must accomplish these tasks under real-world conditions (for example, “the login process should respond within two seconds under peak load” or “all user credentials must be encrypted and stored securely”).
Together, functional and non-functional requirements create a foundation for building great software systems.
NFRs are essential for the following reasons:
Quality of Service: NFRs like response time, availability, and usability directly affect the user’s perception of quality. A system that fulfills its functional requirements but is slow, constantly crashes, or is difficult to use can undermine user trust and satisfaction.
System Stability: Requirements such as reliability, fault tolerance, and recoverability help maintain stable operation even when part of the system fails. Without these, unhandled errors can escalate into large-scale outages.
Security and Compliance: Security-related NFRs dictate how data is protected, how access is controlled, and how audits are conducted. Neglecting these can lead to breaches, legal consequences, or reputational damage.
Scalability and Performance: Requirements for throughput, capacity, and resource utilization ensure the software can handle growth in users or data. If not addressed from the start, scaling can become prohibitively expensive or technically challenging later on.
Maintenance and Evolution: Maintainability, testability, and modularity requirements determine how easily bugs can be fixed, features added, or adaptations made to changing environments. Overlooking them can lead to ballooning technical debt, slowing down future development.
In short, non-functional requirements are not mere “nice-to-haves” but essential components that ensure a software system truly meets user expectations and withstands real-world challenges.
In this article (Part 1), we’ll look at the differences between functional and non-functional requirements. Then, we’ll explore the various trade-offs in NFRs and their architectural impact on building systems.
Functional vs Non-Functional Requirements...
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by "ByteByteGo" <bytebytego@substack.com> - 11:36 - 13 Feb 2025 -
What it takes to achieve—and sustain—growth
On McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
•
Growth-related gaps. For business leaders everywhere, growing profitably—and sustainably—is the ultimate goal. But achieving and maintaining growth is tough work, explain McKinsey Senior Partners Andy West and Jill Zucker and their coauthors. A McKinsey survey of more than 500 leaders (including CEOs) finds that while many respondents believe that they’ve adopted productive mindsets for growth, their attitudes and ambitions don’t always translate into the behaviors and actions that are necessary to enable growth.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:09 - 13 Feb 2025 -
Tomorrow SAP announces a bold new vision.
Tomorrow,
SAP announces a bold new vision.Are you ready for the next era of enterprise management?
Tomorrow at SAP Business Unleashed, SAP leaders, customers, and industry experts will come together to showcase how SAP combines AI, data, and applications like never before to unleash your full potential and make you unstoppable.
What are you waiting for? Register today to unlock your virtual front row seat to hot-off-the press announcements, brand-new product demos, high-value customer stories, and expert Q&A.
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by "SAP Business Unleashed" <sap@mailsap.com> - 11:12 - 12 Feb 2025 -
Explore the state of digital transformation in ASEAN
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Superagency: The transformative potential of AI
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AI in Action: An interactive learning journey is a complement to our Superagency in the workplace report, both inspired by Silicon Valley pioneer Reid Hoffman’s book Superagency: What Could Possibly Go Right with Our AI Future.
Our research finds that the biggest barrier to scaling is not employees—who are ready to incorporate AI into their jobs—but leaders, who are not steering fast enough. This four-part learning series expands on topical themes with rich video conversations and interactive experiences, featuring exclusive content from Hoffman and McKinsey Senior Partner Lareina Yee.
In part one, explore the transformative potential of AI by learning about agentic AI, the cognitive industrial revolution, and more. Regardless of your background with AI, there is much to learn and inspire in the content—we encourage you to immerse yourself in these activities and proceed at your own pace.Subscribe for more
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by "McKinsey & Company" <publishing@email.mckinsey.com> - 02:17 - 12 Feb 2025 -
How to build your leadership muscle
On McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
•
CEO as athlete. Elite athletes and modern CEOs have a lot in common, according to Bob Sternfels, McKinsey’s global managing partner, and Daniel Pacthod, senior partner. Just as modern sports stars are stronger, better coached, and better prepared than those of the past, today’s best leaders have made profound shifts in their thinking about and approach to the job. Indeed, McKinsey research has identified several traits that are required to lead in the 21st century: for example, positive energy, a sense of service, and a belief in continuous learning.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:09 - 12 Feb 2025 -
Drawing Parallels Between Super Bowl LIX and ERP Systems
Drawing Parallels Between Super Bowl LIX and ERP Systems
Hi MD Abul,
As the excitement of Super Bowl LIX settles, where the Philadelphia Eagles triumphed over the Kansas City Chiefs with a score of 40-22, it's a great time to reflect on the parallels between the NFL's top teams and leading ERP systems.
In our blog post, "If ERP Systems Were NFL Teams!", we draw creative analogies between various ERP solutions and NFL franchises. For instance, we compare the Dallas Cowboys to Oracle, highlighting their long-standing success and expansive fan base. Similarly, the New England Patriots are likened to NetSuite, emphasizing their rise to prominence and sustained excellence.
These comparisons offer a unique perspective on how different ERP systems align with the characteristics of NFL teams, providing insights that can guide your organization's digital transformation journey.
To explore these analogies further and see which ERP system aligns with your organization's goals, read the full article here.Best regards,
Eric Kimberling
by "Eric Kimberling" <eric.kimberling@thirdstage-consulting.com> - 05:03 - 11 Feb 2025 -
How Netflix Built a Distributed Counter for Billions of User Interactions
How Netflix Built a Distributed Counter for Billions of User Interactions
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Disclaimer: The details in this post have been derived from the Netflix Tech Blog. All credit for the technical details goes to the Netflix engineering team. The links to the original articles are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.
Netflix operates at an incredible scale, with millions of users interacting with its platform every second.
To provide a great user experience, Netflix needs to track and measure these interactions—for example, counting how many users are watching a new show or clicking on specific features. These numbers help Netflix make real-time decisions about improving user experience, optimizing its infrastructure, and running experiments like A/B tests.
However, counting at such a massive scale is not straightforward. Imagine trying to count millions of events happening worldwide simultaneously while ensuring the results are fast, accurate, and cost-effective.
That’s where the Distributed Counter Abstraction comes in.
This system is designed to handle counting in a way that meets Netflix's demanding requirements:
Low Latency: Counts need to be updated and available within milliseconds.
High Throughput: The system must handle thousands of updates per second without bottlenecks.
Cost Efficiency: Managing such a large-scale system shouldn’t break the bank.
In this article, we’ll look at how Netflix built a Distributed Counter Abstraction and the challenges they faced.
Why the Need for a Distributed Counter?
Netflix needs to count millions of events every second across its global platform. These events could be anything: the number of times a feature is used, how often a show is clicked, or even detailed metrics from experiments like A/B testing
The challenge is that these counting needs aren’t one-size-fits-all.
Some situations require quick, approximate results, while others need precise and durable counts. This is where Netflix’s Distributed Counter Abstraction shines, offering flexibility to address these diverse needs.
There are two broad categories of counting:
Best-Effort Counting: This type of counting is used when speed is more important than accuracy. The counts don’t need to be perfectly precise, and they aren’t stored for a long time. It works well for scenarios like A/B testing, where approximate counts are enough to compare the performance of two groups.
Eventually Consistent Counting: This type is used when accuracy and durability are critical. Although it may take a little longer to get the final count, it ensures the results are correct and preserved. This is ideal for metrics that must be recorded precisely over time, such as billing, regulatory compliance, or critical usage reports.
We will look at both categories in more detail in a later section.
However, both categories share some key requirements that are as follows:
High Availability: The system must always be up and running, even during failures or high demand. Netflix cannot afford downtime in counting since these metrics often drive critical decisions.
High Throughput: The system must handle millions of counting operations per second. This means it needs to efficiently process a huge volume of incoming data without bottlenecks.
Scalability: Netflix operates globally, so the counting system must scale horizontally across multiple regions and handle spikes in usage like during the release of a new show.
The table below shows these requirements in more detail:
Source: Netflix Tech Blog The Counter Abstraction API Design
The Distributed Counter abstraction was designed as a highly configurable and user-friendly system.
The abstraction provides an API that is simple yet powerful, enabling clients to interact with counters consistently. The main API operations are as follows:
1 - Add Count/AddAndGetCount
The purpose of this endpoint is to increment or decrement a counter by a specified value.
The client specifies the namespace (for example, “user_metrics”), the counter name (for example, “views_counter”), and the delta (a positive or negative value to adjust the count). The API returns the updated count immediately after applying the delta.
See the example below:
{ "namespace": "user_metrics", "counter_name": "views_counter", "delta": 5, "idempotency_token": { "token": "unique_event_id", "generation_time": "2025-01-28T14:48:00Z" } }Here, the idempotency token ensures that repeated requests (due to retries) do not result in double-counting.
2 - GetCount
This endpoint helps to retrieve the current value of a counter.
The client specifies the namespace and counter name, and the system fetches the value. Here’s an example API request:
{ "namespace": "user_metrics", "counter_name": "views_counter" }This operation is optimized for speed, returning slightly stale counts in some configurations to maintain performance.
3 - ClearCount
This endpoint helps reset a counter’s value to zero.
Similar to other requests, the client provides the namespace and counter name. This operation also supports idempotency tokens to ensure safe retries.
Counting Techniques
The Distributed Counter abstraction supports several types of counters to meet Netflix's diverse counting requirements. Each approach balances trade-offs between speed, accuracy, durability, and infrastructure cost.
Here’s a detailed breakdown of the main counting strategies:
Best-Effort Regional Counter
This is a lightweight counting approach optimized for speed and low infrastructure cost. It provides quick but approximate counts.
It is built on EVCache, Netflix’s distributed caching solution based on Memcached. Counts are stored as key-value pairs in a cache with minimal latency and high throughput. TTL (Time-To-Live) ensures counters don’t occupy the cache indefinitely.
The best-effort counter is ideal for short-lived experiments like A/B testing where precise counts aren’t critical. The advantages of this type of counter are as follows:
Extremely fast with millisecond-level latency.
Cost-effective due to shared multi-tenant clusters.
However, there are also disadvantages to this approach:
No cross-region replication for counters.
Lacks idempotency, making retries unsafe.
Cannot guarantee consistency across nodes.
Eventually Consistent Global Counter
For scenarios where accuracy and durability are crucial, there are several approaches available under the eventually consistent model. These ensure that counters converge to accurate values over time, though some delays are acceptable.
1 - Single Row Per Counter
It’s a straightforward approach where each counter is represented by a single row in a globally replicated datastore.
See the table below for example:
Despite its simplicity, this approach has some disadvantages such as:
Vulnerable to data loss if an instance fails before flushing its counts.
Difficult to synchronize counter resets across instances.
Lacks idempotency, thereby complicating retries.
2 - Per Instance Aggregation
This approach aggregates counts in memory on individual instances, then periodically writes the aggregated values to a durable store. This process is known as flushing. Introducing sufficient jitter to the flush process helps reduce contention.
See the diagram below for reference:
The main advantage of this approach is that it reduces contention by limiting updates to the durable store. However, it also poses some challenges such as:
Vulnerable to data loss if an instance fails before flushing its counts.
Difficult to synchronize counter resets across instances.
Lacks idempotency.
3 - Durable Queues
This approach logs counter events to a durable queuing system like Apache Kafka. Events are processed in batches for aggregation. Here’s how it works:
Counter events are written to specific Kafka partitions based on a hash of the counter key.
Consumers read from partitions, aggregate events, and store the results in a durable store.
See the diagram below:
This approach is reliable and fault tolerant due to durable logs. Also, idempotency is easier to implement, preventing overcounting during retries.
However, it can cause potential delays if consumers fall behind. Rebalancing partitions as counters or throughput increases can be complex.
4 - Event Log of Increments
This approach logs every individual increment (or decrement) as an event with metadata such as event time and event_id. The event_id can include the source information of where the operation originated.
See the diagram below:
The combination of event_time and event_id can also serve as the idempotency key for the write.
However, this approach also has several drawbacks:
High storage cost due to the need to retain every increment.
Degraded read performance as it requires scanning all events for a counter.
Wide partitions in databases like Cassandra can slow down queries.
Netflix’s Hybrid Approach
Netflix’s counting needs are vast and diverse, requiring a solution that balances speed, accuracy, durability, and scalability.
To meet these demands, Netflix developed a hybrid approach that combines the strengths of various counting techniques we’ve discussed so far. This approach uses event logging, background aggregation, and caching to create a system that is both scalable and efficient, while also maintaining eventual consistency.
Let’s understand the approach in more detail:
1 - Logging Events in the TimeSeries Abstraction
At the core of Netflix’s solution is its TimeSeries Abstraction, a high-performance service designed for managing temporal data.
Netflix leverages this system to log every counter event as an individual record, allowing precise tracking and scalability.
Each counter event is recorded with metadata, including:
event_time: The time the event occurred.
event_id: A unique identifier for the event to ensure idempotency.
event_item_key: Specifies the counter being updated.
Events are organized into time buckets (for example, by minute or hour) to prevent wide partitions in the database. Unique event IDs prevent duplicate counting, even if retries occur.
2 - Aggregation Processes for High Cardinality Counters
To avoid the inefficiency of fetching and recalculating counts from raw events during every read, Netflix employs a background aggregation process. This system continuously consolidates events into summarized counts, reducing storage and read overhead.
Aggregation occurs within defined time windows to ensure data consistency. An immutable window is used, meaning only finalized events (not subject to further updates) are aggregated.
The Last Rollup Timestamp tracks the last time a counter was aggregated. It ensures that the system only processes new events since the previous rollup.
Here’s how the aggregation process works:
The rollup process collects all events for a counter within the aggregation window. It summarizes the total count and updates the rollup store.
Aggregated counts are stored in a durable system like Cassandra for persistence. Future aggregations build on this checkpoint, reducing computation costs.
Rollups are triggered during writes and reads. For writes, it is triggered when a lightweight event notifies the system of changes. During reads, when a user fetches a counter, rollups are triggered if the count is stale.
See the diagram below for the write path process:
Next, we have the diagram below that shows the read or getCount process:
Aggregation reduces the need to repeatedly process raw events, improving read performance. By using immutable windows, Netflix ensures that counts are accurate within a reasonable delay.
3 - Caching for Optimized Reads
While the aggregation process ensures counts are eventually consistent, caching is used to further enhance performance for frequently accessed counters. Netflix integrates EVCache (a distributed caching solution) to store rolled-up counts.
The cache holds the last aggregated count and the corresponding last rollup timestamp. When a counter is read, the cached value is returned immediately, providing a near-real-time response. A background rollup is triggered to ensure the cache stays up to date.
Cached counts allow users to retrieve values in milliseconds, even if they are slightly stale. Also, caching minimizes direct queries to the underlying datastore, saving infrastructure costs.
Key Benefits of the Hybrid Approach
The hybrid approach has several benefits such as:
Combining Accuracy and Performance: It logs every event for precise recounting when needed. Aggregates events in the background to maintain high read performance.
Scales with High Cardinality: Handles millions of counters efficiently using time and event bucketing. This ensures an even distribution of workload across storage and processing systems.
Ensures Reliability: Uses idempotency tokens to handle retries safely. Also, it employs persistent storage (for example, Cassandra) and caches for fault tolerance.
Balances Trade-Offs: Sacrifices some immediacy for eventual consistency in global counts. It also introduces slight delays in aggregation to maintain accuracy within immutable windows.
Scaling the Rollup Pipeline
To manage millions of counters across the globe while maintaining high performance, Netflix uses a Rollup Pipeline. This is a sophisticated system that processes counter events efficiently, aggregates them in the background, and scales to handle massive workloads.
There are three main parts of this rollup pipeline:
1 - Rollup Events and Queues
When a counter is updated (via an AddCount, ClearCount, or GetCount operation), the system generates a lightweight rollup event.
This event notifies the Rollup Pipeline that the counter requires aggregation. The rollup event itself does not include the raw data but merely identifies the counter needing processing.
Here’s how rollup queues work:
Each Rollup Server instance maintains in-memory queues that receive rollup events. These queues allow parallel processing of aggregation tasks, enabling the system to handle high-throughput workloads.
A fast, non-cryptographic hash function (for example, XXHash) ensures that related counters are consistently routed to the same queue. This minimizes duplicate work and improves efficiency.
Multiple events for the same counter are consolidated into a Set, so each counter is rolled up only once within a rollup window.
Netflix opted for in-memory rollup queues to simplify provisioning and reduce costs. This design is easier to implement compared to a fully durable queuing system.
However, there are some potential risks as well.
If a rollup server crashes before processing all events, those events are lost. This is mitigated for frequently accessed counters, as subsequent operations trigger new rollups.
During deployments or scaling, there may be brief overlaps where both old and new servers are active. However, this is managed safely because aggregations occur within immutable windows.
When workloads increase, Netflix scales the Rollup Pipeline by increasing the number of rollup queues and redeploying the rollup servers with updated configurations.
The process is seamless with old servers gracefully shutting down after draining their events. During deployments, both old and new Rollup Servers may briefly handle the same counters. This avoids downtime but introduces slight variability in counts, which is eventually resolved as counts converge.
2 - Dynamic Batching and Back-Pressure
To optimize performance, the Rollup Pipeline processes counters in batches rather than individually.
The size of each batch adjusts dynamically based on system load and counter cardinality. This prevents the system from overwhelming the underlying data store (for example, Cassandra). Within a batch, the pipeline queries the TimeSeries Abstraction in parallel to aggregate events for multiple counters simultaneously.
See the diagram below:
The system monitors the performance of each batch and uses this information to control the processing rate. After processing one batch, the pipeline pauses before starting the next, based on how quickly the previous batch is completed. This adaptive mechanism ensures the system doesn’t overload the storage backend during high traffic.
3 - Handling Convergence for Low and High Cardinality Counters
Low cardinality counters are frequently accessed but have fewer unique instances. The pipeline keeps them in continuous rollup circulation to ensure they remain up to date.
On the other hand, high-cardinality counters have many unique instances (such as per-user metrics) and may not be accessed frequently. To avoid excessive memory usage, the pipeline uses the last-write-timestamp to determine when a counter needs to be re-queued. This ensures aggregation continues until all updates are processed.
See the diagram below:
Centralized Configuration of the Control Plane
At the heart of Netflix’s Distributed Counter Abstraction is its control plane, a centralized system that manages configuration, deployment, and operational complexity across the abstraction layers.
See the diagram below:
The control plane allows Netflix to fine-tune every aspect of the counting service, ensuring it meets the needs of diverse use cases without requiring manual intervention or re-engineering.
1 - Role of the Control Plane
The Control Plane serves as a management hub for all configurations related to the Distributed Counter Abstraction. It is responsible for:
Configuring the persistence mechanisms for counters.
Adjusting settings for specific use cases, such as high or low-cardinality counters.
Implementing strategies for data retention, caching, and durability.
This centralized management ensures that teams across Netflix can focus on their use cases without worrying about the underlying complexities of distributed counting.
2 - Configuring Persistent Mechanisms
The Control Plane enables the configuration of persistence layers for storing counter data.
Netflix uses a combination of EVCache (for caching) and Cassandra (for durable storage). The control plane coordinates their interaction.
EVCache is used for fast, low-latency access to counters. The control plane specifies parameters such as cache size and expiration policies.
{ "id": "CACHE", "physical_storage": { "type": "EVCACHE", "cluster": "evcache_dgw_counter_tier1" } }Cassandra is used as the primary datastore for durable, long-term storage of counters and their rollups.
The configurable parameters for this include:
Keyspace settings, such as the number of partitions.
Time partitioning for counters (for example, breaking data into manageable time buckets).
Retention policies to prevent excessive storage use.
See the example configuration below:
{ "id": "COUNTER_ROLLUP", "physical_storage": { "type": "CASSANDRA", "cluster": "cass_dgw_counter_uc1", "dataset": "my_dataset" } }3 - Supporting Different Cardinality Strategies
Counters can vary greatly in cardinality i.e. the number of unique counters being managed.
Low-cardinality counters are global metrics like “total views” for a show. Such counters are frequently accessed and require continuous rollup processing. This requires smaller time buckets for aggregation and shorter TTLs for cached values to ensure freshness.
High-cardinality counters include per-user metrics like “views per user”. These counters are less frequently accessed but require efficient handling of a large number of unique keys. They involve larger time buckets to reduce database overhead and efficient partitioning to distribute load across storage nodes.
4 - Retention and Lifecycle Policies
Retention policies ensure that counter data does not grow uncontrollably, reducing costs while maintaining historical relevance.
For example, raw counter events are stored temporarily (such as 7 days) before being deleted or archived. Aggregated rollups are retained longer, as they occupy less space and are useful for long-term metrics.
Also, the control plane ensures counters expire after their intended lifespan, preventing them from consuming unnecessary resources.
5 - Multi-Tenant Support
Netflix’s Control Plane is designed to support a multi-tenant environment where different teams or applications can operate their counters independently:
Each namespace is isolated, allowing configurations to vary based on the use case.
For example, the user_metrics namespace may prioritize low-latency caching for real-time dashboards. Also, the billing_metrics namespace may focus on durability and accuracy for financial reporting.
Conclusion
Distributed counting is a complex problem, but Netflix’s approach demonstrates how thoughtful design and engineering can overcome these challenges.
By combining powerful abstractions like the TimeSeries and Data Gateway Control Plane with innovative techniques like rollup pipelines and dynamic batching, Netflix delivers a counting system that is fast, reliable, and cost-effective.
The system processes 75,000 counter requests per second globally while maintaining single-digit millisecond latency for API endpoints. This incredible performance is achieved through careful design choices, including dynamic batching, caching with EVCache, and efficient aggregation processes.
Source: Netflix Tech Blog The principles behind Netflix’s Distributed Counter Abstraction extend well beyond their platform. Any large-scale system requiring real-time metrics, distributed event tracking, or global consistency can benefit from a similar architecture
References:
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