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EP110: Top 5 Strategies to Reduce Latency
EP110: Top 5 Strategies to Reduce Latency
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Top 5 Strategies to Reduce Latency
Load Balancer Realistic Use Cases You May Not Know
Top 4 data sharding algorithms explained
Top 8 C++ Use Cases
Apache Kafka in 100 Seconds
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Top 5 Strategies to Reduce Latency
10 years ago, Amazon found that every 100ms of latency cost them 1% in sales.
That’s a staggering $5.7 billion in today’s terms.
For high-scale user-facing systems, high latency is a big loss of revenue.
Here are the top strategies to reduce latency:
Database Indexing
Caching
Load Balancing
Content Delivery Network
Async Processing
Data Compression
Over to you: What other strategies to reduce latency have you seen?
Load Balancer Realistic Use Cases You May Not Know
Load balancers are inherently dynamic and adaptable, designed to efficiently address multiple purposes and use cases in network traffic and server workload management.
Let's explore some of the use cases:
Failure Handling:
Automatically redirects traffic away from malfunctioning elements to maintain continuous service and reduce service interruptions.Instance Health Checks:
Continuously evaluates the functionality of instances, directing incoming requests exclusively to those that are fully operational and efficient.Platform Specific Routing:
Routes requests from different device types (like mobiles, desktops) to specialized backend systems, providing customized responses based on platform.SSL Termination:
Handles the encryption and decryption of SSL traffic, reducing the processing burden on backend infrastructure.Cross Zone Load Balancing:
Distributes incoming traffic across various geographic or network zones, increasing the system's resilience and capacity for handling large volumes of requests.User Stickiness:
Maintains user session integrity and tailored user interactions by consistently directing requests from specific users to designated backend servers.
Over to you:
Which of these use cases would you consider adding to your network to enhance system reliability and why?Latest articles
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Top 4 Data Sharding Algorithms Explained
We are dealing with massive amounts of data. Often we need to split data into smaller, more manageable pieces, or “shards”. Here are some of the top data sharding algorithms commonly used:
Range-Based Sharding
This involves partitioning data based on a range of values. For example, customer data can be sharded based on alphabetical order of last names, or transaction data can be sharded based on date ranges.Hash-Based Sharding
In this method, a hash function is applied to a shard key chosen from the data (like a customer ID or transaction ID).
This tends to distribute data more evenly across shards compared to range-based sharding. However, we need to choose a proper hash function to avoid hash collision.Consistent Hashing
This is an extension of hash-based sharding that reduces the impact of adding or removing shards. It distributes data more evenly and minimizes the amount of data that needs to be relocated when shards are added or removed.Virtual Bucket Sharding
Data is mapped into virtual buckets, and these buckets are then mapped to physical shards. This two-level mapping allows for more flexible shard management and rebalancing without significant data movement.
Top 8 C++ Use Cases
C++ is a highly versatile programming language that is suitable for a wide range of applications.
Embedded Systems
The language's efficiency and fine control over hardware resources make it excellent for embedded systems development.Game Development
C++ is a staple in the game development industry due to its performance and efficiency.Operating Systems
C++ provides extensive control over system resources and memory, making it ideal for developing operating systems and low-level system utilities.Databases
Many high-performance database systems are implemented in C++ to manage memory efficiently and ensure fast execution of queries.Financial Applications
Web Browsers
C++ is used in the development of web browsers and their components, such as rendering engines.Networking
C++ is often used for developing network devices and simulation tools.Scientific Computing
C++ finds extensive use in scientific computing and engineering applications that require high performance and precise control over computational resources.
Over to you - What did we miss?
Apache Kafka in 100 Seconds
This post is written by guest author Sanaz Zakeri, who is a Senior Software Engineer @Uber.
Apache Kafka is a distributed event streaming platform used for building real-time data processing pipelines and streaming applications. It is highly scalable, fault-tolerant, reliable, and can handle large volumes of data.
In order to understand Kafka, we need to define two terms:
Events: a log of state of something at a specific point in time
Event streams: continuous and unbounded series of events
Kafka can be used as a Messaging in a publish-subscribe model, where producers write event streams, and consumers read the events. This publish-subscribe model enables decoupling of event stream producers and consumers. Also, Kafka can be used as a log aggregation platform, ingesting and storing logs from multiple sources in a durable and fault-tolerant way.
Kafka Components:
Kafka cluster has multiple key components to provide the distributed infrastructure and reliably capture, store, order and provide event streams to client applications.
Brokers:
At the heart of the Kafka cluster lies the brokers which are physical servers that handle event streams. After events are published by producers, the broker makes the events available to consumers. Brokers bring scalability to Kafka as Kafka clusters can span multiple brokers across a variety of infrastructure setup to handle large volumes of events. They also bring fault tolerance since events can be stored and replicated across multiple brokers.
Topics:
Topic is the subject name of where the events are published by producers. Topics can have zero or more consumers listening to them and processing the events.
Partition:
In a topic, data is organized into partitions which store ordered streams of events. Each event within a partition is assigned a unique sequential identifier called offset that represents its position in the partition. Events are appended continually to the partition. A Topics can have one or more partitions. Having more than one partition in a topic enables parallelism as more consumers can read from the topic.
Partitions belonging to a topic can be distributed across separate brokers in the cluster, which brings high data availability and scalability. If one broker fails, the partitions on the remaining brokers can continue to serve data, ensuring fault tolerance.
Producers:
Producers are client applications that write events to Kafka topics as a stream of events.
Consumers:
Consumers are the client applications that subscribe to topics and process or store the events coming to the specific topic. Consumers read events in the order they were received within each partition.
Applications which require real time processing of data will have multiple consumers in a consumer group which can read from partitions on the subscribed topic.
Consumer Groups:
Consumer group is used to organize consumers that are reading a stream of events from one or more topics. Consumer groups enable parallel processing of events and each consumer in the consumer group can read from one partition to enable load balancing on the client application. This functionality not only brings the parallel processing but also brings fault tolerance since if a consumer fails in a consumer group, Partition can be reassigned to another group member.
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by "ByteByteGo" <bytebytego@substack.com> - 11:35 - 4 May 2024 -
The CFO conundrum
by "McKinsey Highlights" <publishing@email.mckinsey.com> - 11:15 - 4 May 2024 -
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by "Sunny Thakur" <sunny.thakur@uffizio.com> - 08:00 - 3 May 2024 -
Why do bad leaders rise to the top?
The Shortlist
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Curated by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
As the tech buzz becomes ever louder, executives are turning back to basics. In the future, many workers will likely be using AI to help them do the technical parts of their jobs. This is likely to free people up for more collaborative work, which means interpersonal skills may become more valuable to executives than ever. This edition, some expect AI to diminish the relative importance of technical skill. Interpersonal skills, therefore, may become more important to executives than ever. This edition of the CEO Shortlist points to new research on why we need more emotionally intelligent leaders and how we can pick them. We also double-click on deep-cut AI concepts, transforming a centuries-old bank, and more. We hope you enjoy the read.
—Liz and Homayoun
Hitting your stride. The early years of a CFO’s tenure are often about scoping out the challenge and pulling together the core team. Midtenure is when the best finance leaders get bold. Our interviews with eight CFOs shed light on how leaders can rise to the occasion.
Reinvent the finance function—and yourself—with “Faster, smarter, bolder: How midtenure CFOs shift into a higher gear,” by Ankur Agrawal, Cristina Catania, Christian Grube, and John Kelleher.
The never-ending story. Business transformation is more than a one-off; it’s an attempt to build a new company on the fly. Successful transformations set their sights far beyond next year’s financial targets, instead thinking decades—or even centuries—into the future. Frankly, most such transformations don’t work. But those that do, as in the case of 240-year-old US bank BNY Mellon, offer inspiration.
What would American founding father Andrew Mellon do? We don’t know, exactly, but we know what leaders of today’s BNY Mellon did. Hear more from Roman Regelman, former senior executive at BNY Mellon, in “Driving long-term business transformation,” by Kevin Carmody.
Your prompt reply is appreciated. Today’s business leaders are no strangers to AI (especially if they’re staying up to date with our publishing). But quick: what does prompt engineering mean for your existing employees? How is tokenization in payments different from tokenization in large language models? Or what needs to happen for AI to be capable of empathy?
Got AI? Then you’ve likely got questions as well. Fill in the gaps in your understanding with “What’s the future of AI?,” a new package of McKinsey Explainers with insights from Michael Chui, Alex Singla, Kate Smaje, Lareina Yee, and many more.
Hot air rises to the top. According to new research, gender—before abilities, competencies, interests, and personalities—is one of the strongest predictors of whether someone reaches a leadership role. And that’s not all. The men who benefit from antimeritocratic discrimination, says author and psychologist Dr. Tomas Chamorro-Premuzic, often exhibit traits that hamper organizational progress.
Listen to the good doctor weigh in on “Why so many bad bosses still rise to the top,” the latest episode of the McKinsey Talks Talent podcast, featuring Chamorro-Premuzic and McKinsey talent leaders Bryan Hancock and Brooke Weddle.We hope you find these ideas inspiring and helpful. See you next time with four more McKinsey ideas for the CEO and others in the C-suite.
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by "McKinsey CEO Shortlist" <publishing@email.mckinsey.com> - 04:46 - 3 May 2024 -
How can employees go from ‘meh’ to motivated?
On Point
Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
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Dissatisfied workers. According to recent McKinsey research, more than half of workers report being relatively dissatisfied with their jobs. That’s a big percentage that strikes at the heart of value creation for organizations that are already facing rising labor costs and declining productivity, McKinsey senior partner Aaron De Smet and coauthors share. Identifying where workers fall along a satisfaction spectrum could help leaders solve the problem.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:05 - 3 May 2024 -
Digital twins, the future of AI, MSMEs, and more: The Daily Read Weekender
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May is here and it’s almost the weekend. Take a breather and dive into the week’s highlights on digital twins, the future of AI, MSMEs, and more.
QUOTE OF THE DAY
chart of the day
Ready to unwind?
—Edited by Joyce Yoo, editor, New York
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A microscope on small businesses: Spotting opportunities to boost productivity
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by "McKinsey & Company" <publishing@email.mckinsey.com> - 02:43 - 2 May 2024 -
Unlocking the Power of SQL Queries for Improved Performance
Unlocking the Power of SQL Queries for Improved Performance
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SQL, or Structured Query Language, is the backbone of modern data management. It enables efficient retrieval, manipulation, and management of data in a Database Management System (DBMS). Each SQL command taps into a complex sequence within a database, building on concepts like the connection pool, query cache, command parser, optimizer, and executor, which we covered in our last issue.
Crafting effective queries is essential. The right SQL can enhance database performance; the wrong one can lead to increased costs and slower responses. In this issue, we focus on strategies such as using the Explain Plan, adding proper indexes, and optimizing commands like COUNT(*) and ORDER BY. We also dive into troubleshooting slow queries.
While MySQL is our primary example, the techniques and strategies discussed are applicable across various database systems. Join us as we refine SQL queries for better performance and cost efficiency.
Explain Plan
In MySQL, the EXPLAIN command, known as EXPLAIN PLAN in systems like Oracle, is a useful tool for analyzing how queries are executed. By adding EXPLAIN before a SELECT statement, MySQL provides information about how it processes the SQL. This output shows the tables involved, operations performed (such as sort, scan, and join), and the indexes used, among other execution details. This tool is particularly useful for optimizing SQL queries, as it helps developers see the query execution plan and identify potential bottlenecks.
When an EXPLAIN statement is executed in MySQL, the database engine simulates the query execution. This simulation generates a detailed report without running the actual query. This report includes several important columns:
id: Identifier for each step in the query execution.
select_type: The type of SELECT operation, like SIMPLE (a basic SELECT without unions or subqueries), SUBQUERY, or UNION.
table: The table involved in a particular part of the query.
type: The join type shows how MySQL joins the tables. Common types include ALL (full table scan), index (index scan), range (index range scan), eq_ref (unique index scan), const/system (constant value optimization).
possible_keys: Potential indexes that might be used.
key: The key (index) chosen by MySQL.
key_len: The length of the chosen key.
ref: Columns or constants used with the key to select rows.
rows: Estimated number of rows MySQL expects to examine when executing the query.
Extra: Additional details, such as the use of temporary tables or filesorts.
Let's explore a practical application of the EXPLAIN command using a database table named orders. Suppose we want to select orders with user_id equal to 100.
SELECT * FROM orders WHERE user_id = 100;To analyze this query with EXPLAIN, we would use:
EXPLAIN SELECT * FROM orders WHERE user_id = 100;The output might look like this:
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by "ByteByteGo" <bytebytego@substack.com> - 11:38 - 2 May 2024 -
New Relic AI monitoring—the industry’s first APM for AI—now generally available
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April 2024
New Relic AI monitoring is now generally available New Relic AI monitoring gives you complete visibility across your entire AI stack just like application performance monitoring. Effortlessly monitor and manage your AI applications confidently with features like automatic instrumentation, deep trace insights into large language model responses, robust data security, and model comparison.
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How BlackLine saved $16 million per year by consolidating toolsConsolidating tools shifted incident culture and created new ways to build code at BlackLine. Here’s how.
Informa runs on New Relic to deliver business value
Informa improves their engineering processes to move beyond performance-based conversations to conversations around how their technology is impacting their business. With observability, Informa is building a culture of continuous improvement.Useful reads
Custom events are a developer's best friendExplore how New Relic custom events can offer deeper insights and enhance operational efficiency for developers.Upcoming Events AWS Summits EMEAWe're thrilled to announce our participation in the upcoming AWS Summits happening in multiple cities across Europe and beyond! Here's where you can catch us.
- Berlin (Booth PO4)
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We'll be showcasing our latest innovations, hosting engaging demos, and offering valuable insights into how New Relic can empower your cloud journey on AWS.
Don't miss this opportunity to connect with us and explore the future of observability and performance monitoring in the cloud!
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New Relic University Online WorkshopsJoin our upcoming New Relic online live training workshops. These 90-minute trainer-led workshops with hands-on labs will help you up level your observability skills.
- Maximising performance with integrated APM and infrastructure monitoring
- 30 May at 10am BST / 11am CEST
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- Maximising observability with New Relic logs
- 27 June at 10am BST / 11am CEST
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New Relic End-of-Life Updates
Legacy synthetics runtimes and CPM (October 22)- New Relic will end-of-life (EOL) our legacy Chrome 72 (and older) and Node 10 (and older) synthetics runtimes and the containerized private minion (CPM).
- Customers will be unable to create new monitors using legacy runtimes on public or private locations as of June 30.
- All customers must be on the new runtime by October 22 in order to prevent synthetic monitoring degradation from occurring.
- See here for more information.
Support for PromQL (July 15)
- We’re standardizing our querying experiences around NRQL by removing PromQL-styled query support.
- You can still access Prometheus metrics and events, but will need to adopt NRQL as the method for querying Prometheus data.
- If you’re currently using PromQL-styled queries to query your data, you’ll need to adopt NRQL.
- Prometheus metrics data will still be accessible in New Relic.
- For more information, see our documentation.
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by "New Relic" <emeamaketing@newrelic.com> - 05:03 - 2 May 2024 -
What’s the state of European grocery retail in 2024?
On Point
Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
•
Lower growth. European grocers faced a challenging year in 2023. Consumers tightened their belts amid rising inflation, leading to a drop in volume and significant downtrading. As a result, industry growth was significantly lower than food price inflation. In Europe, food price inflation averaged 12.8% in 2023, while grocery sales grew at a rate of only 8.6%, McKinsey senior partner Franck Laizet and coauthors share. Still, even though macroeconomic uncertainty will likely persist in 2024, McKinsey data shows some signs of hope for 2024.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:30 - 2 May 2024
