> ## 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-1155 transfers > Create a table containing ERC-1155 Transfers for several, or all token contracts. ERC-1155 is a standard for EVM ecosystems that allows for the creation of both fungible and non-fungible assets within a single contract. The process of transferring ERC-1155 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-1155 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-721 Transfers](/mirror/guides/token-transfers/ERC-721-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 1. Use the readily available ERC-1155 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-1155 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-1155 Transfers Source Dataset Every EVM chain has its own ERC-1155 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-erc1155-transfers.yaml expandable theme={null} name: apex-erc1155-pipeline resource_size: s apiVersion: 3 sources: apex.erc1155_transfers: dataset_name: apex.erc1155_transfers version: 1.3.0 type: dataset start_at: earliest transforms: {} sinks: postgres_apex.erc1155_transfers_public_apex_erc1155_transfers: type: postgres table: apex_erc1155_transfers schema: public secret_name: description: "Postgres sink for Dataset: apex.erc1155_transfers" from: apex.erc1155_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_erc1155_transfers`. If you are use ClickHouse as a sink for this dataset you'll need to add the following schema\_override to avoid potential data precision errors for big numbers, see example: ``` postgres_apex.erc1155_transfers_public_apex_erc1155_transfers: type: clickhouse table: apex_erc1155_transfers secret_name: description: "ClickHouse sink for Dataset: apex.erc1155_transfers" schema_override: amount: UInt256 token_id: UInt256 from: apex.erc1155_transfers ``` You can start the pipeline by running: ```bash theme={null} goldsky pipeline start apex-erc1155-pipeline.yaml ``` Or ```bash theme={null} goldsky pipeline apply apex-erc1155-pipeline.yaml --status ACTIVE ``` That's it! You should soon start seeing ERC-20 token transfers in your database. ## Building ERC-1155 Transfers from scratch using logs In the previous method we just explored, the ERC-20 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-1155 Contract. ### Building ERC-1155 Tranfers using Decoding Transform Functions In this example, we will stream all the `Transfer` events of all the ERC-1155 tokens for the [Scroll chain](https://scroll.io/). To that end, we will dinamically fetch the ABI of the [Rubyscore\_Scroll](https://scrollscan.com/token/0xdc3d8318fbaec2de49281843f5bba22e78338146) token from the Scrollscan API (available [here](https://api.scrollscan.com/api?module=contract\&action=getabi\&address=0xdc3d8318fbaec2de49281843f5bba22e78338146)) and use it to identify all the same events for the tokens in the chain. We have decided to use the ABI of this token for this example but any other ERC-1155 compliant token would also work. ERC-1155 combines the features of ERC-20 and ERC-721 contracts and adds a few features. Each transfer has both a token ID and a value representing the quantity being transfered for funglible tokens, the number `1` for tokens intended to represent NFTs, but how these work depends on how the contract is implemented. ERC-1155 also introduces new event signatures for transfers: `TransferSingle(address,address,address,uint256,uint256)` and `TransferBatch(address,address,address,uint256[],uint256[])` which lets the contract transfer multiple tokens at once to a single recipient. This causes us some trouble since we want one row per transfer in our database, so we'll need some extra SQL logic in our pipeline to deal with this. To mitigate this complexity we have created two different transforms, each dealing with Single and Batch transfers separately. We then aggregate both tables into a single view using a third transform. Let's now see all these concepts applied in an example pipeline definition: #### Pipeline Definition ```yaml scroll-erc1155-transfers.yaml expandable theme={null} name: scroll-erc1155-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 Rubyscore_Scroll token sql: > SELECT *, _gs_log_decode( _gs_fetch_abi('https://api.scrollscan.com/api?module=contract&action=getabi&address=0xdc3d8318fbaec2de49281843f5bba22e78338146&apikey=YOUR_KEY', 'etherscan'), `topics`, `data` ) AS `decoded` 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 erc1155_transfer_single: primary_key: id sql: > SELECT id, address AS contract_address, lower(event_params[2]) AS sender, lower(event_params[3]) AS recipient, COALESCE(TRY_CAST(event_params[4] AS DECIMAL(78)), 0) AS token_id, COALESCE(TRY_CAST(event_params[5] AS DECIMAL(78)), 0) AS amount, block_number, block_hash, log_index, transaction_hash, transaction_index FROM scroll_clean WHERE topics LIKE '0xc3d58168c5ae7397731d063d5bbf3d657854427343f4c083240f7aacaa2d0f62%' erc1155_transfer_batch: primary_key: id sql: > WITH transfer_batch_logs AS ( SELECT *, _gs_split_string_by( REPLACE(TRIM(LEADING '[' FROM TRIM(TRAILING ']' FROM event_params[4])), ',', ' ') ) AS token_ids, _gs_split_string_by( REPLACE(TRIM(LEADING '[' FROM TRIM(TRAILING ']' FROM event_params[5])), ',', ' ') ) AS amounts FROM scroll_clean WHERE topics LIKE '0x4a39dc06d4c0dbc64b70af90fd698a233a518aa5d07e595d983b8c0526c8f7fb%' ) SELECT id || '_' || CAST(t.idx AS STRING) AS `id`, address AS contract_address, lower(event_params[2]) AS sender, lower(event_params[3]) AS recipient, COALESCE(TRY_CAST(token_ids[t.idx] AS DECIMAL(78)),0) AS token_id, COALESCE(TRY_CAST(amounts[t.idx] AS DECIMAL(78)),0) AS amount, block_number, block_hash, log_index, transaction_hash, transaction_index FROM transfer_batch_logs CROSS JOIN UNNEST( CAST( _gs_generate_series( CAST(1 AS BIGINT), CAST(COALESCE(CARDINALITY(token_ids), 0) AS BIGINT) ) AS ARRAY ) ) AS t (idx) scroll_1155_transfers: primary_key: id sql: > SELECT * FROM erc1155_transfer_single UNION ALL SELECT * FROM erc1155_transfer_batch WHERE amount > 0 sinks: scroll_1155_sink: type: postgres table: erc1155_transfers schema: mirror secret_name: description: Postgres sink for ERC1155 transfers from: scroll_1155_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.erc1155_transfers`. There are 5 transforms in this pipeline definition which we'll explain how they work. We'll start from the top: ##### Decoding Transforms ```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 ``` 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. ```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 ``` 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. #### SingleTransfer Transform ```sql Transform: erc1155_transfer_single theme={null} SELECT id, address AS contract_address, lower(event_params[2]) AS sender, lower(event_params[3]) AS recipient, COALESCE(TRY_CAST(event_params[4] AS DECIMAL(78)), 0) AS token_id, COALESCE(TRY_CAST(event_params[5] AS DECIMAL(78)), 0) AS amount, block_number, block_hash, log_index, transaction_hash, transaction_index FROM scroll_clean WHERE topics LIKE '0xc3d58168c5ae7397731d063d5bbf3d657854427343f4c083240f7aacaa2d0f62%' ``` In this transform we focus on SingleTransfer events. Similar to the [ERC-721 example](/mirror/guides/token-transfers/ERC-721-transfers), we use `event_params` we pull out the sender, recipient and token ID, note the indexes we use are different since ERC-1155 tokens have a different `event_signature`. ```sql theme={null} COALESCE(TRY_CAST(event_params[4] AS DECIMAL(78)), 0) AS token_id, COALESCE(TRY_CAST(event_params[5] AS DECIMAL(78)), 0) AS amount, ``` 1. `event_params[4]` is the fourth element of the `event_params` array, and for ERC-1155 this is the token ID. 2. `TRY_CAST(event_params[4] AS NUMERIC)` is casting the string element `event_params[4]` 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[4] AS DECIMAL(78)), 0)`: `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 `0` 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. We repeat this process for `event_params[5]` which represents the amount of a token. ```sql theme={null} AND raw_log.topics LIKE '0xc3d58168c5ae7397731d063d5bbf3d657854427343f4c083240f7aacaa2d0f62%' ``` We filter for a specific topic to get ERC-1155 single transfers, the above topic is for the `event_signature` `TransferSingle(address,address,address,uint256,uint256)`. As with [ERC-721](/mirror/guides/token-transfers/ERC-721-transfers), we could use the event signature as a filter instead. ##### BatchTransfers Transform Now, let's look at the BatchTransfer events: ```Transform: erc1155_transfer_single (subquery) theme={null} WITH transfer_batch_logs AS ( SELECT *, _gs_split_string_by( REPLACE(TRIM(LEADING '[' FROM TRIM(TRAILING ']' FROM event_params[4])), ',', ' ') ) AS token_ids, _gs_split_string_by( REPLACE(TRIM(LEADING '[' FROM TRIM(TRAILING ']' FROM event_params[5])), ',', ' ') ) AS amounts FROM scroll_clean WHERE topics LIKE '0x4a39dc06d4c0dbc64b70af90fd698a233a518aa5d07e595d983b8c0526c8f7fb%' ) ``` The first thing we want to achieve is to decompose the string representation of tokens and their respective amounts into separate rows that we can add as columns to each transaction. This will allow us to index on tokenId-amount pairs much more easily as a second step. This is the trickiest part of the transformation and involves some functionality that is niche to both Goldsky and Flink v1.17. We'll start from the inside and work our way out again. 1. `TRIM(LEADING '[' FROM TRIM(TRAILING ']' FROM event_params[4]))`: Similar to the [ERC-721 example](/mirror/guides/token-transfers/ERC-721-transfers), we use `event_params` to access the token\_id information. For ERC-1155, the string for batch transfers in element 4 looks like this when decoded: \[1 2 3 4 5 6]. We need to trim the leading and trailing \[ and ] characters before splitting it out into individual token IDs. 2. `_gs_split_string_by(...)`: This is a Goldsky UDF which splits strings by the space character only. If you need to split by another character, for now you can use `REGEXP_REPLACE(column, ',', ' ')` to replace commas with spaces. 3. `CROSS JOIN UNNEST ... AS token_ids (token_id)`: This works like `UNNEST` in most other SQL dialects, but is a special case in Flink. It may be confusing that we have two separate CROSS JOINs, but they don't work like CROSS JOIN in other SQL dialects, we'll get a single row with a `token_id` and `token_value` that map correctly to each other. Lastly, we filter on topic: ```sql theme={null} raw_log.topics LIKE '0x4a39dc06d4c0dbc64b70af90fd698a233a518aa5d07e595d983b8c0526c8f7fb%' ``` This is the same as the other topic filters but it is using the topic hash of the batch transfer event signature. Next, onto creating an index for each tokenId - amount pair: ```Transform: erc1155_transfer_single (time series) theme={null} FROM transfer_batch_logs CROSS JOIN UNNEST( CAST( _gs_generate_series( CAST(1 AS BIGINT), CAST(COALESCE(CARDINALITY(token_ids), 0) AS BIGINT) ) AS ARRAY ) ) AS t (idx) ``` In this step we generate a series of indexes that we can use to access each individual tokenId - amount pair within a transfer. We do this by definining a Goldsky UDF called `_gs_generate_series` which will generate an array of indexes for as many tokens there are in the batch. We combine this indexes with our existing table and use to access each token - amount pair: ``` CAST(token_ids[t.idx] AS NUMERIC(78)) as token_id, CAST(amounts[t.idx] AS NUMERIC(78)) as amount, ``` We also use this logic to generate the resulting ID Primary Key for batch transfers: ```sql theme={null} id || '_' || CAST(t.idx AS STRING) AS `id` ``` The `id` coming from the source represents an entire batch transfer event, which can contain multiple tokens, so we concatenate the token\_id to the `id` to make the unnested rows unique. #### Combining Single and Batch Transfers ```Transform: scroll_1155_transfers theme={null} SELECT * FROM erc1155_transfer_single UNION ALL SELECT * FROM erc1155_transfer_batch WHERE amount > 0 ``` This final directive in the third transform creates a combined stream of all single transfers and batch transfers. ### Deploying the pipeline Our last step is to deploy this pipeline and start sinking ERC-1155 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-erc1155-transfers --definition-path scroll-erc1155-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\_0x360fcd6ca8c684039c45642d748735645fac639099d8a89ec57ad2b274407c25\_7 | 0x7de37842bcf314c83afe83a8dab87f85ca3a2cee | 0x0000000000000000000000000000000000000000 | 0x16f6aff7a2d84b802b2ddf0f0aed49033b69f4f9 | 6 | 1 | 105651 | 0x360fcd6ca8c684039c45642d748735645fac639099d8a89ec57ad2b274407c25 | 7 | 0x5907ba72e32434938f45539b2792e4eacf0d141db7c4c101e207c1fb26c99274 | 5 | We can find this [transaction in Scrollscan](https://scrollscan.com/tx/0x0a968c797271d18420261d22f1cef08b040c45b6dd219c9a53f76c1545e592ce). We see that it corresponds to a mint of 60 tokens: This concludes our successful deployment of a Mirror pipeline streaming ERC-1155 Tokens from Scroll chain into our database using inline decoders. Congrats! 🎉 ## Conclusion In this guide, we have learnt how Mirror simplifies streaming ERC-1155 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-1155 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.