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by "McKinsey & Company" <publishing@email.mckinsey.com> - 12:35 - 8 Oct 2024 -
How TikTok Manages A 200K File MonoRepo with Sparo
How TikTok Manages A 200K File MonoRepo with Sparo
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Disclaimer: The details in this post have been derived from the TikTok Developer Blog. All credit for the technical details goes to the TikTok 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.
TikTok, the popular short-form video-sharing platform, has a large and rapidly growing codebase for its web front end. This front-end app is built using TypeScript and is organized as a monorepo - a single Git repository containing code for multiple projects and libraries.
As TikTok's frontend engineering team and feature set grew, so did the size and complexity of this monorepo.
Over time, it expanded to contain over 1,000 separate projects and more than 200,000 source files. While the monorepo architecture provided benefits like shared code and tooling, it also started to cause significant performance issues.
Developers began to notice slowness with common Git operations that are essential to their everyday workflows. These slowdowns were a major drain on productivity, wasting valuable engineering time and ruining the development experience.
The issues stemmed from the sheer size of the codebase that Git had to process for each operation. To address this problem, TikTok's frontend infrastructure team began exploring solutions. They experimented with Git's built-in performance features like partial clones, shallow clones, and Git LFS, but found that they were insufficient for the monorepo's scale and growth rate.
Ultimately, the team developed an in-house tool called Sparo to tackle the monorepo performance challenges. They also eventually made Sparo open-source.
In this article, we'll explore how Sparo works and the benefits it has provided for TikTok's front-end engineering team.
The Problem of Git Slowness in Large Monorepos
A monorepo, short for a monolithic repository, is a software development strategy where a single repository contains multiple projects, libraries, and services, often maintained by different teams. This contrasts with the multi-repo approach, where each project has its separate repository.
The diagram below shows the difference between monorepos, multi-repos, and monolith code bases.
Monorepos have gained popularity among large tech companies like Google, Facebook, and Microsoft for several reasons.
Firstly, monorepos enable better code sharing and reuse across projects, reducing duplication and promoting standardization.
Secondly, they simplify dependency management, as projects within the monorepo can easily reference and use each other.
Thirdly, monorepos provide a unified view of the entire codebase, making it easier to perform cross-cutting changes and maintain a consistent build and test infrastructure.
However, as monorepos grow in size and complexity, Git operations become increasingly slow for everyone.
This is exactly what happened at TikTok. Commands like git clone to download a copy of the repository, git checkout to switch between branches, git status to see current changes, and git commit to save new changes took much longer than they had previously.
Here are some interesting stats from their observations:
Cloning the repository to start working on it could take over 40 minutes for developers with slower network connections. Even on fast connections, a full clone took over 20 minutes.
Checking out a different branch took over a minute and a half.
Just running git status to see the current state of the working copy took 7 seconds, interrupting the flow of developers.
Committing code changes was also painful at around 15 seconds per commit.
How Sparo Improves MonoRepo Performance?
At its core, Sparo leverages two advanced features of Git to dramatically speed up common operations on large monorepos: sparse checkout and partial clone.
Let’s look at both in detail.
Sparse Checkout
Git's sparse checkout feature allows specifying a subset of files or directories to check out from a repository, rather than fetching the entire codebase. Sparo uses this to only check out the files needed to build a given application - namely, the project and its dependencies.
In a monorepo with hundreds or thousands of projects, the files needed for any one project are a relatively small and slow-growing subset compared to the repo as a whole.
By using sparse checkout to limit the working copy to just this subset, Sparo significantly reduces the amount of data that needs to be fetched and the number of files that Git operations need to process. This results in much faster checkout times.
See the diagram below that explains the concept of sparse checkout in Git in which a developer only has to work on the Android Client Development.
Partial Clones
While sparse checkout reduces the number of files in the working copy, a standard Git clone still fetches the contents of every file in the repo and their complete history.
For large repositories, this still means a sizable amount of data transfer and disk usage, even if much of it isn't needed locally.
Git's partial clone feature, enabled by passing the --filter=blob:none option to git clone, optimizes this by only fetching file contents on-demand as they are needed, and excluding objects that aren't reachable by any reference. This reduces the size of the initial clone and speeds up subsequent fetches.
See the diagram below for a visual representation of the same.
Source: GitHub Blog Unlike a shallow clone, the full history is still available if needed, just not eagerly downloaded. Also, unlike Git LFS, partial clone works automatically for all files without a separate storage system.
Additional Sparo Enhancements
Sparo adds some additional enhancements beyond leveraging those two key Git features.
Checkout Profiles
Sparo introduces the concept of checkout profiles, which are predefined sets of directories to include in a sparse checkout. Profiles act as an easy starting point for new hires or contributors to discover what part of the codebase is relevant to a given team.
For example, a frontend team might define a profile that checks out their five most actively developed projects, the dependencies of those projects, and a few additional directories like docs and config.
These profiles are defined in a JSON file and checked into the repo, making it easy for developers to share profiles and quickly set up their working copy to match their team's standard environment.
See the example of the profile file:
{ "selections": [ { "selector": "--to", "argument": "project-a" }, { "selector": "--to", "argument": "project-b" }, { "selector": "folder", "argument": "docs" } ] }This profile would help the developers checkout project-a, project-b, and the docs folder.
With the profile created, developers can use sparo checkout --profile to checkout the repository according to the profile. This checks out only the specified projects and folders, significantly reducing the data fetched and the time taken.
Mirrored Commands
To make adoption frictionless, Sparo provides its command line interface that aims to be a drop-in replacement for the standard Git CLI. In other words, Sparo is fully compatible with Git, so teams can incrementally adopt it while still interoperating with standard Git usage.
By mirroring the Git interface, Sparo minimizes the learning curve and allows it to be gradually adopted into existing workflows. Familiar commands like clone, checkout, status, add, commit, etc. are all provided with the same syntax as their Git equivalents.
Developers can use the Sparo versions of commands to take advantage of the optimized performance, while still falling back to the regular Git commands if needed for advanced cases.
Under the hood, the Sparo versions of these commands automatically set the appropriate configurations to enable partial clone and sparse checkout based on the active profile. They also allow collecting anonymous usage telemetry to power dashboards for monitoring the adoption and performance of the tool.
Sparo Performance Gains
The TikTok team saw dramatic performance improvements after adopting Sparo:
clone time dropped from 23 minutes to just 2 minutes
checkout time went from 1.5 minutes to 30 seconds
status command time reduced from 7 seconds to 1 second
commit time improved from 15 seconds to 11 seconds
These improvements have a huge impact on developer productivity and experience. Instead of waiting minutes for Git operations to complete, developers can iterate and commit changes quickly, maintaining their flow and focus.
Conclusion
As monorepos grow in size and complexity, maintaining good performance for common Git operations becomes increasingly challenging. TikTok's frontend team experienced this firsthand as their TypeScript monorepo surpassed 1,000 projects and 200,000 source files, leading to significant slowdowns in commands like clone, checkout, status, and commit.
To address this problem, the TikTok team developed Sparo, an open-source tool that leverages Git's sparse checkout and partial clone features to speed up Git operations on large monorepos.
The TikTok engineering team's journey with Sparo highlights the importance of proactively addressing performance issues in rapidly growing codebases.
Sparo's roadmap includes a telemetry plugin system for monitoring and support for additional front-end build systems. As more companies encounter performance challenges with huge monorepos, tools like Sparo can become increasingly important for maintaining developer productivity.
References:
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Why is everyone talking about observability?
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How to accelerate decarbonization to meet net-zero targets
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Air Charter Flight// Speed Up Your Supply Chain with Our International Air Freight From China To Mexico
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Stay ambitious and committed: A leader’s guide to supporting women at work
Leading Off
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Welcome to the latest edition of Leading Off. 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
A decade ago, we conducted our first wave of research with LeanIn.Org on the state of women in corporate America and what women’s day-to-day work experiences were really like. After hearing from more than 480,000 employees over the past ten years, we have learned so much about what’s working (and what’s not) with companies’ efforts to make the workplace fairer and more inclusive. In short, there’s still much more work to do. This week, we reflect on the gains that women and their companies have made, why women’s experiences are not so different from what they were years ago, and the ways in which companies can make sustainable progress toward gender parity.
During our ten years of research on the corporate pipeline, many companies have taken action on workplace diversity, and important gains have been made. Yet according to McKinsey senior partners Alexis Krivkovich and Lareina Yee and their coauthors, the findings from the tenth-anniversary Women in the Workplace report underscore the persistent obstacles that women confront in the workplace. Progress, in other words, remains fragile—especially for women of color—and parity for all women, according to our projections, is almost 50 years away. In many ways, women’s outlook, as well as their day-to-day experiences (dealing with microaggressions, for example), are the same or worse than when we started keeping tabs. True progress requires a renewed commitment from companies and an expanded playbook, including both practical steps that foster inclusion (such as debiasing hiring and promotion processes and activating employees to curb bias) and broader, systemic changes that are rooted in new mindsets and behaviors about women’s contributions and potential at work.
That’s the share of employees at large companies who, in a survey about motivation and performance management, said their managers did not have the sufficient skills to conduct performance reviews. According to McKinsey senior partners Brooke Weddle and Dana Maor and their coauthors, “employees were significantly more motivated by performance reviews when they were offered by a skilled manager and reflected the achievement of a performance goal.” The survey indicates that formal evaluations matter for both employee engagement and advancement—where women are already at a disadvantage, given the state of the corporate pipeline—and it’s one of the many ways managers can actively shape their employees’ work experiences, including career development.
That’s NYU professor of psychology and author Tessa West on how people can reclaim agency in a toxic workplace. In a conversation about toxic behaviors and burnout with McKinsey’s Jacqueline Brassey, West explains that most of us will eventually experience toxic behaviors at work—and that many feel disempowered to respond to or raise these issues with our colleagues. Such behaviors include microaggressions: daily slights that are rooted in bias, undermine women’s abilities, and contribute to stress, burnout, and turnover. Microaggressions are also one aspect of women’s work experience that, according to this year’s Women in the Workplace research, has not improved over time.
It stands to reason that people are chosen for leadership roles after demonstrating the qualities that would ultimately make them good leaders: integrity, humility, and empathy, to name a few. Not so, according to Dr. Tomas Chamorro-Premuzic. In an interview with McKinsey’s Brooke Weddle and Bryan Hancock, he shares that organizations tend to favor characteristics such as overconfidence, narcissism, and even incompetence in candidates for leadership roles, who (still) tend to be men more often than women. If these habits seem counterproductive for both men and women in the workplace, they are. So, what’s the cure? Dr. Chamorro-Premuzic recommends that companies “ignore everything that is style and not substance” and focus more on potential leaders’ soft skills, as well as traits that make other people better—a defining characteristic of leadership. “If you look at that, then you’re going to have a selection of individuals who are predisposed for leadership roles and who look very different from the majority of leaders today.”
The relationship between managers and direct reports is central to the work experience and critical to employee satisfaction. Good bosses are compassionate and curious about their people, help employees balance the demands of work and life, and support their advancement. In addition to practicing empathy, gratitude, and positivity with others, leaders can grow by looking inward. When leaders are self-aware, reflective, and focused on personal growth and self-improvement (the more “micro” the habit is, the better), they’re more equipped to help their employees and to make the workplace better for everyone.
Lead by supporting the women in your workplace.
— Edited by Daniella Seiler, executive editor, Washington, DC
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by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 04:44 - 7 Oct 2024 -
Navigating a path to the data-driven enterprise of 2030
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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.
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Data-driven future. By 2030, data will be embedded in systems, processes, channels, and interactions that drive automated actions. This shift toward “data ubiquity” requires companies to make substantial changes to become truly data-driven. For example, those looking to unlock “alpha”—or achieve returns above benchmark levels—should focus on integrating data and technologies, among other strategies, say McKinsey senior partners Holger Harreis and Kayvaun Rowshankish and colleagues in a new McKinsey Quarterly article.
—Edited by Jana Zabkova, senior editor, New York
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 11:10 - 6 Oct 2024 -
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by "Remote" <hello@remote-comms.com> - 08:59 - 6 Oct 2024 -
The week in charts
The Week in Charts
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by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 03:29 - 5 Oct 2024 -
Preparing for a new era of technological change
Monthly Highlights
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Technology officers are adapting their strategies to ensure their organizations capitalize on the transformative trends impacting global businesses. This is a pivotal opportunity for growth, but it demands clear focus and capability development. In our featured story, McKinsey’s Aamer Baig, Jeffrey Lewis, Klemens Hjartar, Rob Cain, and Sven Blumberg reveal a set of shifts that tech officers can make to meet the demands of business leaders. Our second featured story helps data leaders think through seven essential priorities that reflect the most important changes that are occurring, what the main complexities are, and where executives can focus their energy to realize the data-driven enterprise of 2030. Other highlights include the following topics:
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progress, decline, and stagnation in women’s representation and experiences in the workplace
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how to successfully navigate the next phase of the energy transition
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leadership lessons and the future of AI from the CEO of Dell Technologies, Michael Dell
•
three steps to get more employees involved in and committed to a transformation
Women in the Workplace 2024: The 10th-anniversary report
In the tenth year of our Women in the Workplace research, in partnership with LeanIn.Org, we reflect on the notable gains women have made—and how their experiences at work are, in many ways, the same or worse than ten years ago.
Recommendations for companies
Global Energy Perspective 2024
As the global energy transition enters a new phase, our Global Energy Perspective 2024 presents a data-driven view of the possible road ahead.
Read the report
Direct from Michael Dell: Leadership lessons and the future of AI
The CEO of Dell Technologies weighs in on what’s next for AI, how companies can successfully transform in the face of constant disruption, and how his leadership style has changed over 40 years.
See the interview
Going all in: Why employee ‘will’ can make or break transformations
Tapping the organization’s collective energy to implement change is crucial for transformation success. A three-step process creates the momentum to meet the challenges ahead.
Learn the 3 E’s
Five fundamental truths: How B2B winners keep growing
Our latest B2B Pulse Survey reveals that across all sectors and regions, market leaders continue to experiment, invest, and commit to omnichannel sales as the path to sustainable growth.
Plus, 3 archetypes of decision makers
Leading from the inside out: Why CEOs must make time for self-reflection
The authors of a new book on the journey of leadership explain why this era of unprecedented global change demands a new style of leadership—one that reflects the inner world of the CEO along with the needs of organizations and employees.
Leader, know thyself
McKinsey Explainers
Find direct answers to complex questions, backed by McKinsey’s expert insights.
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McKinsey Themes
Browse our essential reading on the topics that matter.
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McKinsey on Books
Explore this month’s best-selling business books prepared exclusively for McKinsey Publishing by Circana.
See the lists— Curated by Eleni Kostopoulos, managing editor, New York
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by "McKinsey Highlights" <publishing@email.mckinsey.com> - 11:57 - 5 Oct 2024 -
EP132: Big O Notation 101: The Secret to Writing Efficient Algorithms
EP132: Big O Notation 101: The Secret to Writing Efficient Algorithms
Forwarded this email? Subscribe here for moreThis week’s system design refresher:
Big O Notation 101: The Secret to Writing Efficient Algorithms
Top 4 Forms of Authentication Mechanisms
8 Key Concepts in DDD
Top 9 NoSQL Database Use Cases
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Big O Notation 101: The Secret to Writing Efficient Algorithms
From simple array operations to complex sorting algorithms, understanding the Big O Notation is critical for building high-performance software solutions.
O(1)
This is the constant time notation. The runtime remains steady regardless of input size. For example, accessing an element in an array by index and inserting/deleting an element in a hash table.O(n)
Linear time notation. The runtime grows in direct proportion to the input size. For example, finding the max or min element in an unsorted array.O(log n)
Logarithmic time notation. The runtime increases slowly as the input grows. For example, a binary search on a sorted array and operations on balanced binary search trees.O(n^2)
Quadratic time notation. The runtime grows exponentially with input size. For example, simple sorting algorithms like bubble sort, insertion sort, and selection sort.O(n^3)
Cubic time notation. The runtime escalates rapidly as the input size increases. For example, multiplying two dense matrices using the naive algorithm.O(n logn)
Linearithmic time notation. This is a blend of linear and logarithmic growth. For example, efficient sorting algorithms like merge sort, quick sort, and heap sortO(2^n)
Exponential time notation. The runtime doubles with each new input element. For example, recursive algorithms solve problems by dividing them into multiple subproblems.O(n!)
Factorial time notation. Runtime skyrockets with input size. For example, permutation-generation problems.O(sqrt(n))
Square root time notation. Runtime increases relative to the input’s square root. For example, searching within a range such as the Sieve of Eratosthenes for finding all primes up to n.
Over to you: What else will you add to better understand the Big O Notation?
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Top 4 Forms of Authentication Mechanisms
SSH Keys:
Cryptographic keys are used to access remote systems and servers securelyOAuth Tokens:
Tokens that provide limited access to user data on third-party applicationsSSL Certificates:
Digital certificates ensure secure and encrypted communication between servers and clientsCredentials:
User authentication information is used to verify and grant access to various systems and services
Over to you: How do you manage those security keys? Is it a good idea to put them in a GitHub repository?
8 Key Concepts in DDD
Domain Driven Design
Domain-driven design advocates driving the design of software through domain modeling.
Unified language is one of the key concepts of domain-driven design. A domain model is a bridge across the business domains.Business Entities
The use of models can assist in expressing business concepts and knowledge and in guiding further development of software, such as databases, APIs, etc.Model Boundaries
Loose boundaries among sets of domain models are used to model business correlations.Aggregation
An Aggregate is a cluster of related objects (entities and value objects) that are treated as a single unit for the purpose of data changes.Entities vs. Value Objects
In addition to aggregate roots and entities, there are some models that look like disposable, they don't have their own ID to identify them, but are more as part of some entity that expresses a collection of several fields.Operational Modeling
In domain-driven design, in order to manipulate these models, there are a number of objects that act as "operators".Layering the architecture
In order to better organize the various objects in a project, we need to simplify the complexity of complex projects by layering them like a computer network.Build the domain model
Many methods have been invented to extract domain models from business knowledge.
Top 9 NoSQL Database Use Cases
Different databases excel in different areas and it’s important to choose the right database for the requirement.
MongoDB (Document Store)
Used for content management systems and catalog management. Features BSON format, schema-less design, supports horizontal scaling with sharding, and high availability with replicationCassandra (Wide-column Store)
Ideal for time-series data management and recommendation engines. Offers wide-column format, distributed architecture, and CQL for SQL-like querying.Redis (Key-Value Store)
Suited for Cache, Session Management, and Gaming Leaderboards. Provides in-memory storage, support for complex data structures, and persistence options with RDB and AOF.Couchbase (Document Store with Key-Value)
Used for content management systems and e-commerce platforms. Combines key-value and document-based operations with memory-first architecture and cross-data center replication.Neo4j (Graph DB)
Excellent for social networking and fraud detection. Features ACID compliance, index-free adjacency, Cypher Query Language, and HA cluster capabilities.Amazon DynamoDB (Key-Value and Document)
Perfect for serverless and IoT applications. Supports both key-value and complex document data, managed by AWS, with features like partition data across nodes and DynamoDB streams.Apache Hbase (Wide-Column Store)
Used for data warehouse and large-scale data processing. Modeled after Google’s Bigtable, offers Hadoop integration, auto-sharding, strong consistency, and region servers.Elasticsearch (Search Engine)
Ideal for full-text search and log and event data analysis. Built on Apache Lucene, document-oriented, with sharding and replication capabilities, and a RESTful interface.CouchDB (Document Store)
Suitable for mobile applications and CMS. Document-oriented, ensures data consistency without locking, supports eventual consistency, and uses a RESTful API.
Over to you: Which other NoSQL database would you add to the list?
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by "ByteByteGo" <bytebytego@substack.com> - 11:35 - 5 Oct 2024 -
A net-zero reality check
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by "McKinsey & Company" <publishing@email.mckinsey.com> - 12:15 - 4 Oct 2024 -
Is the world facing a state of permacrisis?
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by "McKinsey & Company" <publishing@email.mckinsey.com> - 10:45 - 4 Oct 2024
