How Magic Eden handles enormous traffic as one of the most popular crypto dApps

Alex Danilychev / Engineer at Magic Eden
As the leading cross-chain NFT platform, Magic Eden handles a significant amount of daily traffic both through our core website as well as our API that we expose to the community. In 2021, Magic Eden processed over 1.5x more transactions than OpenSea, with one transaction every 1-3 seconds.
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We recently underwent a redesign of our Solana indexing system this year and are proud to make the following improvements:

  1. 10x faster wallet page loads
  2. 10x faster e2e latency (duration from user seeing a transaction succeed to when our site reflects this information) 
  3. Significant cost savings 


Overall p99 of our API across all endpoints: 

One of our more problematic endpoints - major reduction from p50 - p99: 


Transaction indexing speed improvements: 

Recently, we’ve hit up to 4000 queries per second across all of our APIs. Our APIs for fetching NFTs for specific collections hit 100qps, 500qps at peak. Our system handles 300 transactions a second at peak, with transactions reflecting on the site from the blockchain within seconds.

We’ll go into the details of the architecture behind these improvements in subsequent posts, but let’s set the table on Solana development.


Web3 Development 

How Web3 development differs from traditional web development 

There are plenty of in-depth descriptions of the difference between a traditional webApp and a dApp (application built on top of the blockchain). Some of my favorites include Web2 vs Web3 by DriftWeb2 vs Web3 by the Ethereum Organd Architecture of a Web3 App.

For now we’ll focus on the largest difference: the application data source

In a traditional web application, the core data for the application will be stored in a database managed by the application developer. The application developer has full control over the data modeling, table schemas, and workloads. The application developer can decide the choice of database, and optimizations like indexing, normalization, and caching based on the workloads they expect their app to take. All applications reads and writes pass through the same stack and database. 

In web3 - that core data is stored in a decentralized, globally available, permissionless database called a blockchain. All application writes that are initiated by the user go to the blockchain, not the developer’s database. The developer can deploy new programs to that blockchain, but the fundamental properties of being distributed and general-purpose can bog down a vanilla webApp without additional optimization.


To recap:,YITYP6Jky9Hmmo7bgVJNGQ 

 Reads Writes
WebAppApp Database App Database 
dApp (no backend) Blockchain RPC ProviderBlockchain RPC Provider
dApp (custom backend) App ServerBlockchain RPC Provider








Example Web3 dApp

Let’s say you want to build a dApp that allows users to stake Solana with your program. 


This will involve setting up your frontend - typical HTML, CSS, Javascript, potentially with a frontend framework like React. You’d then write your smart contract (Anchor is popular) then deploy it to the chain.  The final step is to integrate your app to write and read from the Solana blockchain via web3js, and deploy!  


At this point, you’ve managed to build your dApp, get it deployed, and start serving users! You didn’t even need to deploy a backend server. RPC providers, backed by the blockchain, handle your read/write traffic and get you functional quickly.

Things are going well, but as you scale your user base, you hit a wall with performance. It becomes challenging to add new analytical features like a leaderboard for most prolific stakers. You check the network log and notice that getProgramAccounts takes an incredibly long time to fetch the token accounts relevant to the users on your site!  


Solana Storage Model   


Solana is a write-optimized chain, meaning it is designed to prioritize the speed and efficiency of transactions - write operations that modify the state of the blockchain. Solana achieves this by using a unique proof-of-history (PoH) consensus algorithm, which allows it to process thousands of transactions per second. Its core design principles are performance, scalability, and reliability.

Solana’s account model is fairly simple - optimized for fast reads by account and owner addresses. Most information for each account is stored in a data field as a serialized byte array: 

This storage makes it difficult to query for any data actually contained by each account, since the data is serialized and unindexed! 


So if we’d like to execute a query for all tokens a user owns, we’d need to query getProgramAccounts for the Token Program, filtering down for tokens owned by the user’s wallet address. This sounds convenient, but the underlying database does not index the Token owner field, because it is in the serialized byte array data field. This means we must scan over all accounts owned by the token program, an O(n) scan! And N is in the hundreds of millions in this case.. 


Many other common Solana use cases rely on this call pattern: 

  1. Enumerate all NFTs in a collection
  2. Get all listings on Magic Eden for a specific user 
  3. Find the rarest NFT in a user’s wallet ,04xy8ClwJtLn6mmh1PzPRA 


Clearly there’s room for optimization here - accounts should be indexed in a way specific to the schemas of the serialized data they contain. Solana’s RocksDB does not do this for efficiency/abstraction - it’s reminiscent of how blocks are laid out on disc in a typical file system.

Custom Indexing of Solana data


So we’re now at the point in our web3 dApp when we want to bootstrap a backend database that will store and index data in a deserialized format. This database will allow our application to quickly find the data we want for our frontend. Going back to our need to fetch all tokens that a user has staked, we’ll now have a new endpoint:

GET /app/user_wallet/:wallet_address/staked

Which will hit our database w/ a query similar to 

SELECT token_amount FROM app_stakes WHERE wallet = :wallet_address

Since we’ve indexed the wallet column of the app_stakes table, lookup becomes O(1)! We no longer need to wait for our target RPC provider to iterate through all token accounts to find our user’s stake!


Other Self-Indexing Motivations


There are other reasons we may want to index on-chain data ourselves:


A common pattern in database design is denormalization - increasing redundancy of our stored data to avoid joins and provide faster, specialized reads for various use cases. On Solana - certain entities we’re interested in tracking may have multiple accounts that constitute a single entity. 

Let’s take an NFT for example - NFTs on Solana are constituted by a MintAccount, TokenAccount, a Metadata account, and additional off-chain data.   


Fetching all of this data via RPC in a frontend would be arduous. Indexing all accounts in our own DB as separate tables means reads are still not as efficient as possible, since we require joins. Denormalizing this info into a single “NFT” table makes reads faster and more efficient. 



Indexing data ourselves allows us to support custom aggregations. Both OLTP (like Postgres, Cockroach)  and OLAP (like Pinot) databases could be great options here to allow fast reads for aggregations, like the token-staker leaderboard: 

SELECT wallet, SUM(*) as stakedTokens

FROM app_stakes

GROUP BY wallet

ORDER BY stakedTokens  


Indexing via RPC

Common practice in the Solana ecosystem to maintain a database reflecting on-chain data is by utilizing RPC nodes to backfill the entities we’re interested in, then process transactions for those entities in order to pick up new mutations as they come in. 

Solana RPC nodes come with a Subscription Websocket API which consumers can subscribe for updates to various updates. 

We can use the slotSubscribe websocket to get updated when the network has produced new blocks, then getBlock to retrieve all transactions in the block for that slot, filter down to the transactions for accounts we’re interested in, then fetch all recent account data for the accounts that we’ve observed to have changed.,u7S-eua1QpJ1wjo5kg8OJg 


This method works reasonably well, and is extensible to new transaction types and new account types we may see. 


However, there can be some drawbacks if we’re interested in knowing the up-to-date state of accounts on-chain: 


RPC Performance

The same performance issues we were having in our dApp with RPC calls like getProgramAccounts are still happening - we’ve just moved our RPC callsite from the frontend to our backend. We’ve unlocked efficient reads for our users, but all data must travel over the wire, and we’re bound to the same RPC interface on the indexing path like we were calling before. 

Backfills still require getProgramAccounts, and account updates must be fetched. 

Imagine streaming 100 GB of data in HTTP calls that return 100kb at a time - this is what’s happening when fetching (and backfilling) account data from RPC 

In addition, RPC nodes generally serve multiple functions beyond read traffic. Many RPC nodes submit client transactions to the network and participate in consensus. These supplemental responsibilities slow down RPC nodes and stop them from excelling at serving read traffic, and can end up falling behind the network.    


RPC nodes don’t support pagination on most endpoints. This means that APIs have limited scalability as the underlying data-size grows, making endpoints slower to load and more resource intensive.    

Enter Geyser

This is where Geyser - a validator plugin for data streaming - comes in. We’ll discuss Geyser details, the motivations behind integrating it into your stack, and some popular plugins in the next post.

The information provided on this website is provided for general educational purposes only and is in no way financial or investment advice. Certain information may have also been provided to us or prepared by third parties; these materials are provided for convenience and are not an endorsement by Magic Eden. Magic Eden is not liable for any errors, changes or amendments to such information, including any actions taken in reliance on such information.