> ## Documentation Index > Fetch the complete documentation index at: https://docs.goldsky.com/llms.txt > Use this file to discover all available pages before exploring further. # ERC-721 transfers > Create a table containing ERC-721 Transfers for several or all token contracts ERC-721 tokens, also known as NFTs, provide a standardized format for non-fungible digital assets within EVM ecosystems. The process of transferring ERC-721 tokens into a database is fundamental, unlocking opportunities for data analysis, tracking, and the development of innovative solutions. This guide is part of a series of tutorials on how you can stream transfer data into your datawarehouse using Mirror pipelines. Here we will be focusing on ERC-721 Transfers, visit the following two other guides for other types of Transfers: * [Native Transfers](/mirror/guides/token-transfers/native-transfers) * [ERC-20 Transfers](/mirror/guides/token-transfers/ERC-20-transfers) * [ERC-1155 Transfers](/mirror/guides/token-transfers/ERC-1155-transfers) ## What you'll need 1. A Goldky account and the CLI installed 1) Install the Goldsky CLI: **For macOS/Linux:** ```shell theme={null} curl https://goldsky.com | sh ``` **For Windows:** ```shell theme={null} npm install -g @goldskycom/cli ``` Windows users need to have Node.js and npm installed first. Download from [nodejs.org](https://nodejs.org) if not already installed. 2) Go to your [Project Settings](https://app.goldsky.com/dashboard/settings) page and create an API key. 3) Back in your Goldsky CLI, log into your Project by running the command `goldsky login` and paste your API key. 4) Now that you are logged in, run `goldsky` to get started: ```shell theme={null} goldsky ``` 2. A basic understanding of the [Mirror product](/mirror) 3. A destination sink to write your data to. In this example, we will use [the PostgreSQL Sink](/mirror/sinks/postgres) ## Introduction In order to stream all the ERC-721 Transfers of a chain there are two potential methods available: 1. Use the readily available ERC-20 dataset for the chain you are interested in: this is the easiest and quickest method to get you streaming token transfers into your sink of choice with minimum code. 2. Build the ERC-20 Transfers pipeline from scratch using raw or decoded logs: this method takes more code and time to implement but it's a great way to learn about how you can use decoding functions in case you want to build more customized pipelines. Let's explore both method below with more detail: ## Using the ERC-20 Transfers Source Dataset Every EVM chain has its own ERC-20 dataset available for you to use as source in your pipelines. You can check this by running the `goldsky dataset list` command and finding the EVM of your choice. For this example, let's use `apex` chain and create a simple pipeline definition using its ERC-20 dataset that writes the data into a PostgreSQL instance: ```yaml apex-erc721-tokens.yaml expandable theme={null} name: apex-erc721-pipeline resource_size: s apiVersion: 3 sources: apex.erc721_transfers: dataset_name: apex.erc721_transfers version: 1.0.0 type: dataset start_at: earliest transforms: {} sinks: postgres_apex.erc721_transfers_public_apex_erc721_transfers: type: postgres table: apex_erc721_transfers schema: public secret_name: description: 'Postgres sink for Dataset: apex.erc721_transfers' from: apex.erc721_transfers ``` If you copy and use this configuration file, make sure to update: 1. Your `secretName`. If you already [created a secret](/mirror/manage-secrets), you can find it via the [CLI command](/reference/cli#secret) `goldsky secret list`. 2. The schema and table you want the data written to, by default it writes to `public.apex_erc721_transfers`. You can start the pipeline by running: ```bash theme={null} goldsky pipeline start apex-erc721-pipeline.yaml ``` Or ```bash theme={null} goldsky pipeline apply apex-erc721-pipeline.yaml --status ACTIVE ``` That's it! You should soon start seeing ERC-721 token transfers in your database. ## Building ERC-721 Transfers from scratch using logs In the previous method we just explored, the ERC-721 datasets that we used as source to the pipeline encapsulates all the decoding logic that's explained in this section. Read on if you are interested in learning how it's implemented in case you want to consider extending or modifying this logic yourself. To build the token transfers pipeline from scratch, use the `raw_logs` Direct Indexing dataset for that chain in combination with [Decoding Transform Functions](/reference/mirror-functions/decoding-functions) using the ABI of a specific ERC-721 Contract. ### Building ERC-721 Tranfers using Decoding Transform Functions In this example, we will stream all the `Transfer` events of all the ERC-721 tokens for the [Scroll chain](https://scroll.io/). To that end, we will dinamically fetch the ABI of the [Cosmic Surprise](https://scrollscan.com/token/0xcf7f37b4916ac5c530c863f8c8bb26ec1e8d2ccb) token from the Scrollscan API (available [here](https://api.scrollscan.com/api?module=contract\&action=getabi\&address=0xcf7f37b4916ac5c530c863f8c8bb26ec1e8d2ccb)) and use it to identify all the same events for the tokens in the chain. We have decided to use the ABI of this NFT contract for this example but any other ERC-721 compliant token would also work. We need to differentiate ERC-20 token transfers from ERC-721 (NFT) transfers since they have the same event signature in decoded data: `Transfer(address,address,uint256)`. However, if we look closely at their event definitions we can appreciate that the number of topics differ: * [ERC-20](https://ethereum.org/en/developers/docs/standards/tokens/erc-20/): `event Transfer(address indexed _from, address indexed _to, uint256 _value)` * [ERC-721](https://ethereum.org/en/developers/docs/standards/tokens/erc-721/): `event Transfer(address indexed _from, address indexed _to, uint256 indexed _tokenId)` ERC-20 Transfer events have three topics (one topic for event signature + 2 topics for the indexed params). NFTs on the other hand have four topics as they have one more indexed param in the event signature. We will use this as a filter in our pipeline transform to only index ERC-721 Transfer events. Let's now see all these concepts applied in an example pipeline definition: #### Pipeline Definition ```yaml scroll-erc721-transfers.yaml expandable theme={null} name: scroll-erc721-transfers apiVersion: 3 sources: my_scroll_mainnet_raw_logs: type: dataset dataset_name: scroll_mainnet.raw_logs version: 1.0.0 transforms: scroll_decoded: primary_key: id # Fetch the ABI from scrollscan for Cosmic Surprise token sql: > SELECT *, _gs_log_decode( _gs_fetch_abi('https://api.scrollscan.com/api?module=contract&action=getabi&address=0xcf7f37b4916ac5c530c863f8c8bb26ec1e8d2ccb&apikey=YOUR_KEY', 'etherscan'), `topics`, `data` ) AS `decoded` WHERE topics LIKE '0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef%' AND SPLIT_INDEX(topics, ',', 3) IS NOT NULL FROM my_scroll_mainnet_raw_logs scroll_clean: primary_key: id # Clean up the previous transform, unnest the values from the `decoded` object sql: > SELECT *, decoded.event_params AS `event_params`, decoded.event_signature AS `event_name` FROM scroll_decoded WHERE decoded IS NOT NULL AND decoded.event_signature = 'Transfer' scroll_721_transfers: primary_key: id sql: > SELECT id, address AS contract_address, lower(event_params[1]) AS sender, lower(event_params[2]) AS recipient, COALESCE(TRY_CAST(event_params[3] AS NUMERIC), -999) AS token_id, event_name, block_number, block_hash, log_index, transaction_hash, transaction_index FROM scroll_clean sinks: scroll_721_sink: type: postgres table: erc721_transfers schema: mirror secret_name: description: Postgres sink for ERC721 transfers from: scroll_721_transfers ``` If you copy and use this configuration file, make sure to update: 1. Your `secretName`. If you already [created a secret](/mirror/manage-secrets), you can find it via the [CLI command](/reference/cli#secret) `goldsky secret list`. 2. The schema and table you want the data written to, by default it writes to `mirror.erc721_transfers`. There are 3 transforms in this pipeline definition which we'll explain how they work: ```sql Transform: scroll_decoded theme={null} SELECT *, _gs_log_decode( _gs_fetch_abi('https://api.scrollscan.com/api?module=contract&action=getabi&address=0xc7d86908ccf644db7c69437d5852cedbc1ad3f69&apikey=YOUR_KEY', 'etherscan'), `topics`, `data` ) AS `decoded` FROM scroll_mainnet.raw_logs WHERE topics LIKE '0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef%' AND SPLIT_INDEX(topics, ',', 3) IS NOT NULL ``` As explained in the [Decoding Contract Events guide](/mirror/guides/decoding-contract-events) we first make use of the `_gs_fetch_abi` function to get the ABI from Scrollscan and pass it as first argument to the function `_gs_log_decode` to decode its topics and data. We store the result in a `decoded` [ROW](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/table/types/#row) which we unnest on the next transform. We include the topic and `SPLIT_INDEX` filters here to limit decoding only to the relevant events. * `topics LIKE '0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef%'`: `topics` is a comma separated string. Each value in the string is a hash. The first is the hash of the full event\_signature (including arguments), in our case `Transfer(address,address,uint256)` for ERC-721, which is hashed to `0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef`. We use `LIKE` to only consider the first signature, with a `%` at the end, which acts as a wildcard. * `SPLIT_INDEX(topics, ',', 3) IS NOT NULL`: as mentioned in the introduction, ERC-20 transfers share the same `event_signature` as ERC-721 transfers. The difference between them is the number of topics associated with the event. ERC-721 transfers have four topics, and ERC-20 transfers have three. ```sql Transform: scroll_clean theme={null} SELECT *, decoded.event_params AS `event_params`, decoded.event_signature AS `event_name` FROM scroll_decoded WHERE decoded IS NOT NULL AND decoded.event_signature = 'Transfer' ``` In this second transform, we take the `event_params` and `event_signature` from the result of the decoding. We then filter the query on: * `decoded IS NOT NULL`: to leave out potential null results from the decoder * `decoded.event_signature = 'Transfer'`: the decoder will output the event name as event\_signature, excluding its arguments. We use it to filter only for Transfer events. ```sql Transform: scroll_721_transfers theme={null} SELECT id, address AS contract_address, lower(event_params[1]) AS sender, lower(event_params[2]) AS recipient, COALESCE(TRY_CAST(event_params[3] AS NUMERIC), -999) AS token_id, event_name, block_number, block_hash, log_index, transaction_hash, transaction_index FROM scroll_clean ``` In this last transform we are essentially selecting all the Transfer information we are interested in having in our database. We've included a number of columns that you may or may not need, the main columns needed for most purposes are: `id`, `contract_address` (if you are syncing multiple contract addresses), `sender`, `recipient` and `token_id`. * `id`: This is the Goldsky provided `id`, it is a string composed of the dataset name, block hash, and log index, which is unique per event, here's an example: `log_0x60eaf5a2ab37c73cf1f3bbd32fc17f2709953192b530d75aadc521111f476d6c_18` * `address AS contract_address`: We use the lower function here to lower-case the address to make using this data simpler downstream, we also rename the column to contract\_address to make it more explicit. * `lower(event_params[1]) AS sender`: Here we continue to lower-case values for consistency. In this case we're using the first element of the `event_params` array (using a 1-based index), and renaming it to `sender`. Each event parameter maps to an argument to the `event_signature`. * `lower(event_params[2]) AS recipient`: Like the previous column, we're pulling the second element in the `event_params` array and renaming it to `recipient`. For the token\_id we introduce a few SQL functions `COALESCE(TRY_CAST(event_params[3] AS NUMERIC), -999) AS token_id`. We'll start from the inside and work our way out. 1. `event_params[3]` is the third element of the `event_params` array, and for ERC-721 this is the token ID. Although not covered in this example, since ERC-20 shares the same signature, this element represents a token balance rather than token ID if you're decoding ERC-20 transfers. 2. `TRY_CAST(event_params[3] AS NUMERIC)` is casting the string element `event_params[3]` to `NUMERIC` - token IDs can be as large as an unsigned 256 bit integer, so make sure your database can handle that, if not, you can cast it to a different data type that your sink can handle. We use `TRY_CAST` because it will prevent the pipeline from failing in case the cast fails returning a `NULL` value instead. 3. `COALESCE(TRY_CAST(event_params[3] AS NUMERIC), -999)`: `COALESCE` can take an arbitrary number of arguments and returns the first non-NULL value. Since `TRY_CAST` can return a `NULL` we're returning `-999` in case it does. This isn't strictly necessary but is useful to do in case you want to find offending values that were unable to be cast. Lastly, we are also adding more block metadata to the query to add context to each transaction: ``` event_name, block_number, block_hash, log_index, transaction_hash, transaction_index ``` It's worth mentioning that in this example we are interested in all the ERC-721 Transfer events but if you would like to filter for specific contract addresses you could simply add a `WHERE` filter to this query with address you are interested in, like: `WHERE address IN ('0xBC4CA0EdA7647A8aB7C2061c2E118A18a936f13D', '0xdac17f958d2ee523a2206206994597c13d831ec7')` #### Deploying the pipeline Our last step is to deploy this pipeline and start sinking ERC-721 transfer data into our database. Assuming we are using the same file name for the pipeline configuration as in this example, we can use the [CLI pipeline create command](/reference/cli#pipeline-create) like this: `goldsky pipeline create scroll-erc721-transfers --definition-path scroll-erc721-transfers.yaml` After some time, you should see the pipeline start streaming Transfer data into your sink. Remember that you can always speed up the streaming process by [updating](/reference/cli#pipeline-update) the resourceSize of the pipeline Here's an example transfer record from our sink: | id | contract\_address | sender | recipient | token\_id | event\_name | block\_number | block\_hash | log\_index | transaction\_hash | transaction\_index | | --------------------------------------------------------------------------- | ------------------------------------------ | ------------------------------------------ | ------------------------------------------ | --------- | ----------- | ------------- | ------------------------------------------------------------------ | ---------- | ------------------------------------------------------------------ | ------------------ | | log\_0x5e3225c40254dd5b1b709152feafaa8437e505ae54c028b6d433362150f99986\_34 | 0x6e55472109e6abe4054a8e8b8d9edffcb31032c5 | 0xd2cda3fa01d34878bbe6496c7327b3781d4422bc | 0x6e55472109e6abe4054a8e8b8d9edffcb31032c5 | 38087399 | Transfer | 4057598 | 0x5e3225c40254dd5b1b709152feafaa8437e505ae54c028b6d433362150f99986 | 34 | 0xf06c42ffd407bb9abba8f00d4a42cb7f1acc1725c604b8895cdb5f785f827967 | 11 | We can find this [transaction in Scrollscan](https://scrollscan.com/tx/0xf06c42ffd407bb9abba8f00d4a42cb7f1acc1725c604b8895cdb5f785f827967). We see that it corresponds to the transfer of MERK token: This concludes our successful deployment of a Mirror pipeline streaming ERC-721 Tokens from Scroll chain into our database using inline decoders. Congrats! 🎉 ## Conclusion In this guide, we have learnt how Mirror simplifies streaming NFT Transfer events into your database. We have first looked into the easy way of achieving this, simply by making use of the readily available ERC-721 dataset of the EVM chain and using it as the source to our pipeline. We have also deep dived into the standard decoding method using Decoding Transform Functions, implementing an example on Scroll chain. With Mirror, developers gain flexibility and efficiency in integrating blockchain data, opening up new possibilities for applications and insights. Experience the transformative power of Mirror today and redefine your approach to blockchain data integration. Can't find what you're looking for? Reach out to us at [support@goldsky.com](mailto:support@goldsky.com) for help.