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A Crash Course on Scaling the API Layer
A Crash Course on Scaling the API Layer
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The API (Application Programming Interface) layer serves as the backbone for communication between clients and the backend services in modern internet-based applications.
It acts as the primary interface through which clients, such as web or mobile applications, access the functionality and data provided by the application. The API Layer of any application has several key responsibilities such as:
Process incoming requests from clients based on the defined API contract.
Enforce security mechanisms and protocols by authenticating and authorizing clients based on their credentials or access tokens.
Orchestrate interactions between various backend services and aggregate the responses received from them.
Handle responses by formatting and returning the result to the client.
Due to the central role APIs play in the application architecture, they become critical for the application scalability.
The scalability of the API layer is crucial due to the following reasons:
Handling Load and Traffic Spikes: As applications become popular, they encounter increased traffic and sudden spikes in user demand. A scalable API can manage the increased load efficiently.
Better User Experience: The bar for user expectation has gone up. Most users these days expect fast and responsive applications. A scalable API ensures that the application can support a high number of users without compromising performance.
Cost and Resource Optimization: Scalable APIs unlock the path to better resource utilization. Rather than provisioning the infrastructure upfront for the highest demand level, instances are added and removed based on demand, resulting in reduced operational costs.
In this article, we’ll learn the key concepts a developer must understand for API scalability. We will also look at some tried and tested strategies for scaling the API layer with basic code examples for clarity. Lastly, we will also look at some best practices that can help with scaling the API layer.
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Today’s energy system is a huge and deeply interlinked physical entity with around 60,000 power plants, two million kilometers of oil and gas pipelines, and 1.5 billion vehicles on the road. Despite the momentum of recent years, the energy transition is in its early stages. Only 10 percent of the low-emissions power capacity needed by 2050 to meet global climate commitments is currently deployed. Navigating the remaining 90 percent requires confronting the reality that the energy transition is at its core a physical transformation—on a colossal scale.
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Trillions of Indexes: How Uber’s LedgerStore Supports Such Massive Scale
Trillions of Indexes: How Uber’s LedgerStore Supports Such Massive Scale
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Ledgers are the source of truth of any financial event. By their very nature, ledgers are immutable. Also, we usually want to access data stored in these ledgers in various combinations.
With billions of trips and deliveries, Uber performs tens of billions of financial transactions. Merchants, riders, and customers are involved in these financial transactions. Money flows from the ones spending to the ones earning.
To manage this mountain of financial transaction data, the LedgerStore is an extremely critical storage solution for Uber. The myriad access patterns for the data stored in LedgerStore also create the need for a huge number of indexes.
In this post, we’ll look at how Uber implemented the indexing architecture for LedgerStore to handle trillions of indexes and how they migrated a trillion entries of Uber’s Ledger Data from DynamoDB to LedgerStore.
What is LedgerStore?
LedgerStore is Uber’s custom-built storage solution for managing financial transactions. Think of it as a giant, super-secure digital ledger book that keeps track of every financial event at Uber, from ride payments to food delivery charges.
What makes LedgerStore special is its ability to handle an enormous amount of data. We’re talking about trillions of entries.
Two main features supported by LedgerStore are:
Immutability: LedgerStore is designed to be immutable, which means once a record is written, it cannot be changed. This ensures the integrity of financial data.
Indexes: LedgerStore allows quick look-up of information using various types of indexes. For example, if someone needs to check all transactions for a particular user or all payments made on a specific date, LedgerStore can retrieve this information efficiently.
Ultimately, LedgerStore helps Uber manage its financial data more effectively, reducing costs compared to previous solutions.
Types of Indexes
LedgerStore supports three main types of indexes:
Strongly consistent indexes
Eventually consistent indexes
Time-range indexes
Let’s look at each of them in more detail.
Strongly Consistent Indexes
These indexes provide immediate read-your-write guarantees, crucial for scenarios like credit card authorization flows. For example, when a rider starts an Uber trip, a credit card hold is placed, which must be immediately visible to prevent duplicate charges.
See the diagram below that shows the credit-card payment flow for an Uber trip supported by strongly consistent indexes.
If the index is not strongly consistent, the hold may take a while to be visible upon reading. This can result in duplicate charges on the user’s credit card.
Strongly consistent indexes at Uber are built using the two-phase commit approach. Here’s how this approach works in the write path and read path.
1 - The Write Path
The write path consists of the following steps:
When a new record needs to be inserted, the system first writes an “index intent” to the index table. It could even be multiple indexes.
This intent signifies that a new record is about to be written. If the index intent write fails, the whole insert operation fails.
After the index intent is successfully written, the actual record is written to the main data store.
If the record write is also successful, the system commits the index. This is done asynchronously to avoid affecting the end-user insert latency.
The diagram below shows this entire process.
There is one special case to consider here: if the index intent write succeeds, but the record write fails, the index intent has to be rolled back to prevent the accumulation of unused intents. This part is handled during the read path.
2 - The Read Path
The below steps happen during the read path:
If a committed index entry is found, the response data is returned to the client.
If an index entry is found but with a “pending” status, the system must resolve its state. This is done by checking the main data store for the corresponding record.
If the record exists, the index is asynchronously committed and the record is returned to the user.
If the record doesn’t exist, the index intent is deleted or rolled back and the read operation does not return a result for the query.
The diagram below shows the process steps in more detail.
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Eventually Consistent Indexes
These indexes are designed for scenarios where real-time consistency is not critical, and a slight delay in index updates is acceptable. They offer better performance and availability at the cost of immediate consistency.
From a technical implementation point of view, the eventually consistent indexes are generated using the Materialized Views feature of Uber’s Docstore.
Materialized views are pre-computed result sets stored as a table, based on a query against the base table(s). The materialized view is updated asynchronously when the base table changes.
When a write occurs to the main data store, it doesn’t immediately update the index. Instead, a separate process periodically scans for changes and updates the materialized view. The consistency window is configurable and determines how frequently the background process runs to update the indexes.
In Uber’s case, the Payment History Display screen uses the eventually consistent indexes.
Source: Uber Engineering Blog Time-range Indexes
Time-range indexes are a crucial component of LedgerStore, designed to query data within specific time ranges efficiently.
These indexes are important for operations like offloading older ledger data to cold storage or sealing data for compliance purposes. The main characteristic of these indexes is their ability to handle deterministic time-range batch queries that are significantly larger in scope compared to other index types.
Earlier, the time-range indexes were implemented using a dual-table design approach in DynamoDB. However, it was operationally complex.
The migration of LedgerStore to Uber’s Docstore paved the path for a simpler implementation of the time-range index. Here’s a detailed look at the Docstore implementation for the time-range indexes:
Single Table Design: Only one table is used for time-range indexes in Docstore.
Partitioning Strategy: Index entries are partitioned based on full timestamp value. This ensures a uniform distribution of writes across partitions, eliminating the chances of hot partitions and write throttling.
Sorted Data Storage: Data is stored in a sorted manner based on the primary key (partition + sort keys).
Read Operation: Reads involve a prefix scanning of each shard of the table up to a certain time granularity. For example, to read 30 minutes of data, the operation might be broken down into three 10-minute interval scans. Each scan is bounded by start and end timestamps. After scanning, a scatter-gather operation is performed, followed by sort merging across shards to obtain all time-range index entries in the given window, in a sorted fashion.
For clarity, consider a query to fetch all ledger entries between “2024-08-09 10:00:00” and “2024-08-09 10:30:00”. The query would be broken down into three 10-minute scans:
2024-08-09 10:00:00 to 2024-08-09 10:10:00
2024-08-09 10:10:00 to 2024-08-09 10:20:00
2024-08-09 10:20:00 to 2024-08-09 10:30:00
Each of these scans would be executed across all shards in parallel. The results would then be merged and sorted to provide the final result set.
The diagram below shows the overall process:
Index Lifecycle Management
Index lifecycle management is another component of LedgerStore’s architecture that handles the design, modification, and decommissioning of indexes.
Let’s look at the main parts of the index lifecycle management system.
Index Lifecycle State Machine
This component orchestrates the entire lifecycle of an index:
Creating the index table
Backfilling it with historical index entries
Validating the entries for completeness
Swapping the old index with the new one for read/write operations
Decommissioning the old index
The state machine ensures that each step is completed correctly before moving to the next, maintaining data integrity throughout the process.
The diagram below shows all the steps:
Historical Index Data Backfill
When new indexes are defined or existing ones are modified, it’s essential to backfill historical data to ensure completeness.
The historical index data backfill component performs the following tasks:
Builds indexes from historical data stored in cold storage.
Backfills the data to the storage layer in a scalable manner.
Uses configurable rate-limiting and batching to manage the process efficiently.
Index Validation
After indexes are backfilled, they need to be verified for completeness. This is done through an offline job that:
Computes order-independent checksums at a certain time-window granularity.
Compares these checksums across the source of truth data and the index table.
From a technical point of view, the component uses a time-window approach i.e. computing checksums for every 1-hour block of data. Even if a single entry is missed, the aggregate checksum for that time window will lead to a mismatch.
For example, If checksums are computed for 1-hour blocks, and an entry from 2:30 PM is missing, the checksum for the 2:00 PM - 3:00 PM block will not match.
Migration of Uber’s Payment Data to LedgerStore
Now that we have understood about LedgerStore’s indexing architecture and capabilities, let’s look at the massive migration of Uber’s payment data to LedgerStore.
Uber’s payment platform, Gulfstream, was initially launched in 2017 using Amazon DynamoDB for storage. However, as Uber’s operations grew, this setup became increasingly expensive and complex.
By 2021, Gulfstream was using a combination of three storage systems:
DynamoDB for the most recent 12 weeks of data. This was the hot data.
TerraBlob (Uber’s internal blob store like AWS S3) for older or cold data.
LedgerStore (LSG) where new data was being written and where they wanted to migrate all data.
The primary reasons for migrating to LedgerStore were as follows:
Cost savings: Moving to LedgerStore promised significant recurring cost savings compared to DynamoDB.
Simplification: Consolidating from three storage systems to one would simplify the code and design of the Gulfstream services.
Improved Performance: LedgerStore offered shorter indexing lag and reduced latency due to being on-premise.
Purpose-Built Design: LedgerStore was specifically designed for storing payment-style data, offering features like verifiable immutability and tiered storage for cost management.
The migration was a massive undertaking. Some statistics are as follows:
1.2 Petabytes of compressed data
Over 1 trillion entries
0.5 PB of uncompressed data for secondary indexes.
For reference, storing this data on typical 1 TB hard drives requires a total of 1200 hard drives just for the compressed data.
Checks
One of the key goals of the migration was to ensure that the backfill was correct and acceptable. Also, the current traffic requirements needed to be fulfilled.
Key validation methods adopted were as follows:
1 - Shadow Validation
This ensured that the new LedgerStore system could handle current traffic patterns without disruption.
Here’s how it worked:
The system would compare responses from the existing DynamoDB-based system with what the LedgerStore would return for the same queries. This allowed the team to catch any discrepancies in real time.
An ambitious goal was to ensure 99.99% completeness and correctness with an upper bound of 99.9999%.
To achieve six nines of confidence, the team needed to compare at least 100 million records. At a rate of 1000 comparisons per second, this would take more than a day to collect sufficient data.
During shadow validation, production traffic was duplicated on LedgerStore. This helped the team verify the LedgerStore’s ability to handle the production load.
2 - Offline Validation
While shadow validation was effective for current traffic patterns, it couldn’t provide strong guarantees about rarely-accessed historical data. This is where offline validation came into play.
Here’s how it worked:
Offline validation involved comparing complete data dumps from DynamoDB with the data in LedgerStore. The largest offline validation job compared 760 billion records, involving 70 TB of compressed data.
The team used Apache Spark for these massive comparison jobs, leveraging distributed shuffle-as-a-service for Spark.
Backfill Issues
The process of migrating Uber’s massive ledger data from DynamoDB to LedgerStore involved several backfill challenges that had to be solved:
Scalability: The engineering team learned that starting small and gradually increasing the scale was crucial. Blindly pushing beyond the system’s limit could create a DDoS attack on their systems.
Incremental Backfills: Given the enormous volume of data, attempting to backfill all at once would generate 10 times the normal traffic load. The solution was to break the backfill into smaller, manageable batches that could be completed within minutes.
Rate Control: To ensure consistent behavior during backfill, the team implemented rate control using Guava’s RateLimiter in Java/Scala. The team also developed a system to dynamically adjust the backfill rate based on the current system load. For this, they used an additive increase/multiplicative decrease approach similar to TCP congestion control.
Data File Size Management: The team found that managing the size of data files was important. They aimed to keep the file sizes around 1 GB, with flexibility between 100 MB and 10 GB. This approach helped avoid issues like MultiPart limitations in various tools and prevented problems associated with having too many small files.
Fault Tolerance: Data quality issues and data corruption were inevitable. The team’s solution was to monitor statistics. If the failure rate was high, they would manually stop the backfill, fix the issue, and continue. For less frequent issues, they let the backfill continue while addressing problems in parallel.
Logging Challenges: Excessive logging during backfill could overload the logging infrastructure. The solution was to use a rate limiter for logging. For example, they might log only once every 30 seconds for routine operations while logging all errors if they occurred infrequently.
Conclusion
The impact of Uber’s ledger data migration to LedgerStore has been amazing, with over 2 trillion unique indexes successfully transferred without a single data inconsistency detected in over six months of production use.
This migration, involving 1.2 PB of compressed data and over 1 trillion entries, showcases Uber’s ability to handle massive-scale data operations without disrupting critical financial processes. It also provides great learning points for the readers.
The cost savings from this migration have been substantial, with estimated yearly savings exceeding $6 million due to reduced spend on DynamoDB. Performance improvements have been notable, with LedgerStore offering shorter indexing lag and better network latency due to its on-premise deployment within Uber’s data centers.
References:
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EP125: How does Garbage Collection work?
EP125: How does Garbage Collection work?
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2. Parallel Garbage Collector: Also known as the "Throughput Collector."
3. CMS (Concurrent Mark-Sweep) Garbage Collector: Low-latency collector aiming to minimize pause times.
4. G1 (Garbage-First) Garbage Collector: Aims to balance throughput and latency.
5. Z Garbage Collector (ZGC): A low-latency garbage collector designed for applications that require large heap sizes and minimal pause times.Python
Python's garbage collection is based on reference counting and a cyclic garbage collector:
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2. Cyclic Garbage Collector: Handles circular references that can't be resolved by reference counting.GoLang
Concurrent Mark-and-Sweep Garbage Collector: Go's garbage collector operates concurrently with the application, minimizing stop-the-world pauses.
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A Cheat Sheet for Designing Fault-Tolerant Systems
Designing fault-tolerant systems is crucial for ensuring high availability and reliability in various applications. Here are six top principles of designing fault-tolerant systems:
Replication
Replication involves creating multiple copies of data or services across different nodes or locations.Redundancy
Redundancy refers to having additional components or systems that can take over in case of a failure.Load Balancing
Load balancing distributes incoming network traffic across multiple servers to ensure no single server becomes a point of failure.Failover Mechanisms
Failover mechanisms automatically switch to a standby system or component when the primary one fails.Graceful Degradation
Graceful degradation ensures that a system continues to operate at reduced functionality rather than completely failing when some components fail.Monitoring and Alerting
Continuously monitor the system's health and performance, and set up alerts for any anomalies or failures.
10 System Design Tradeoffs You Cannot Ignore
If you don’t know trade-offs, you DON'T KNOW system design.
Vertical vs Horizontal Scaling
Vertical scaling is adding more resources (CPU, RAM) to an existing server.Horizontal scaling means adding more servers to the pool.
SQL vs NoSQL
SQL databases organize data into tables of rows and columns.NoSQL is ideal for applications that need a flexible schema.
Batch vs Stream Processing
Batch processing involves collecting data and processing it all at once. For example, daily billing processes.Stream processing processes data in real time. For example, fraud detection processes.
Normalization vs Denormalization
Normalization splits data into related tables to ensure that each piece of information is stored only once.Denormalization combines data into fewer tables for better query performance.
Consistency vs Availability
Consistency is the assurance of getting the most recent data every single time.Availability is about ensuring that the system is always up and running, even if some parts are having problems.
Strong vs Eventual Consistency
Strong consistency is when data updates are immediately reflected.Eventual consistency is when data updates are delayed before being available across nodes.
REST vs GraphQL
With REST endpoints, you gather data by accessing multiple endpoints.With GraphQL, you get more efficient data fetching with specific queries but the design cost is higher.
Stateful vs Stateless
A stateful system remembers past interactions.A stateless system does not keep track of past interactions.
Read-Through vs Write-Through Cache
A read-through cache loads data from the database in case of a cache miss.A write-through cache simultaneously writes data updates to the cache and storage.
Sync vs Async Processing
In synchronous processing, tasks are performed one after another.In asynchronous processing, tasks can run in the background. New tasks can be started without waiting for a new task.
Over to you: Which other tradeoffs have you encountered?
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A Crash Course on Architectural Scalability
A Crash Course on Architectural Scalability
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In today's interconnected world, software applications have a global reach, serving users from diverse geographical locations.
With the rapid growth of social media and viral content, a single tweet or post can lead to a sudden and massive surge in traffic to an application. The importance of building applications with scalable architecture from the ground up has never been higher.
Being prepared for unexpected traffic spikes is indispensable for development teams building applications. A sudden increase in user demand may be just around the corner. Not being prepared for it can put immense pressure on the application's infrastructure. It not only causes performance degradation but can also, in some cases, result in a complete system failure.
To mitigate these risks and ensure a good user experience, teams must proactively design and build scalable architectures.
But what makes scalability such a desirable characteristic?
Scalability allows the application to dynamically adapt to changing workload requirements without compromising performance or availability.
In this post, we’ll understand the true meaning of scalability from different perspectives followed by the various techniques and principles that can help you scale the application’s architecture. Also, in subsequent posts in the coming weeks, we’ll take deeper dives into the scalability of each layer and component of a typical architecture.
What is Scalability?...
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