Evgeny medvedev ethereum

Exporting contracts Contracts in Ethereum are created with a special kind of transaction in which the receiver is set to 0x0. The receipt for such a transaction contains a contractAddress field with the address of the created contract. Disassembling the bytecode with ethereum-dasm allows getting the initialization block of the contract and all PUSH4 opcodes in it.

Below is an example output of ethereum-dasm : Screenshot of ethereum-dasm. These 4-byte hashes serve as function IDs in transactions that are calls to contracts. For example, the ID of the transfer address,uint function is 0xacbb, so transactions invoking this method will contain 0xacbb as a prefix in the data field. Given that we can get all function IDs in a contract, we can tell if it conforms to some interface, such as ERC If all of the methods , defined in the standard, are present in a contract, we mark it as matching or belonging to type ERC The result of exporting contracts and the other export tasks in Airflow are the files in a Cloud Storage bucket, partitioned by day.

Atomicity and idempotence We wanted to make sure all operations in the workflow are atomic and idempotent , as these properties are necessary to guarantee the consistency of the data. Idempotency means you can run an operation multiple times and the result will be as if you ran it just once. It allows Airflow to retry the tasks without keeping or checking any state. All export tasks are idempotent because the output files will be overwritten in the Cloud Storage bucket, after multiple runs.

BigQuery load and query operations are atomic and idempotent, which means there will be no case in which query results are incorrect, even during the process of loading new data. Fortunately, load operations are free in BigQuery. Without it you can end up having duplicate or missing rows in your tables.

Another obstacle to ensuring consistency is chain reorganizations in Ethereum. This is a situation when a temporary fork happens in the blockchain, which could make blocks previously streamed to BigQuery stale. We plan to use streaming alongside daily tasks, which will load and replace past-day partitions approach called Lambda Architecture.

This will make the data eventually consistent — only the last day may have duplicates, missing rows, and stale blocks, while all the previous days will be strongly consistent. The feature in BigQuery that allows this is the ability to load and query a single partition. Query You can access the dataset here. Check out this blog post for the examples. We at CoinFi use this dataset to compute ERC20 token metrics: token velocity, number of unique wallets, token retention rate, token distribution, and more.

You can use it to disassemble the CoinFi smart contract: lang-sql Loading The query that demonstrates this, as well as the ability to work with arrays and structs in BigQuery, can be found here. You can try to do many other things with UDFs, for instance parse transaction inputs, parse event logs, or even run EVM!

Just kidding about that last one. Check out other interesting queries and implementation details in my blog. Future plans The next big feature we are going to work on is support for message calls a. After years of working with blockchain data, we found that many of our clients wanted the same thing: to know more about the wallets that are transacting on-chain. So we started building Nansen in to address this need, and have quickly become a leading provider of data and analytics for Ethereum.

How do you get the wallet labels? We use a variety of methods to label wallets, including Heuristics and algorithms Investigations and research by our team User submissions.. We aim for extremely high precision, meaning we'd rather not label an address than label it incorrectly.

One key thing to realize is that there is a very strong network effect in adding wallet labels. The consequence is that every wallet label we add to our platform can help us infer even more wallet labels. For the on-chain data itself, we rely heavily on the open source project Ethereum ETL. What about privacy? This is a common question, and the first thing to make clear is that we do not focus on personal individuals in our wallet labels.

One important source of data for Nansen is the blockchain itself. It's important to understand that blockchains are public and immutable. We have no control of what users decide to publish on them. A relevant example is the Ethereum Name Service ENS , which lets users purchase a human-readable name for their wallet e.

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Addresses can be not only be wallets that hold balances, but can also contain smart contract bytecode that allows the programmatic creation of agreements and automatic triggering of their execution. An aggregate of coordinated smart contracts could be used to build a decentralized autonomous organization. The Ethereum blockchain data are now available for exploration with BigQuery. The Ethereum ETL project on GitHub contains all source code used to extract data from the Ethereum blockchain and load it into BigQuery—we welcome more contributors and more blockchains!

Why make Ethereum blockchain data available on Google Cloud? Accordingly, we built a software system on Google Cloud that: Synchronizes the Ethereum blockchain to computers running Parity in Google Cloud. Performs a daily extraction of data from the Ethereum blockchain ledger, including the results of smart contract transactions, such as token transfers. De-normalizes and stores date-partitioned data to BigQuery for easy and cost-effective exploration. The Ethereum blockchain dataset is also available on Kaggle here.

Fork this example kernel to experiment with your own copy of the Python code. Interesting queries and analyses Below, we show a number of interesting queries and visualizations based on the Ethereum dataset.

Our analyses focus on three popular topics: Smart contract function calls On-chain transaction time-series and transaction networks Smart contract function analytics Analysis 1: Popular Smart Contracts Event Logs The main use case for the Ethereum blockchain has so far been the exchange of digital tokens.

View my query here. The most popular ERC smart contract by transaction count is 0xc8cf97bead5deaeff8e7ad , the main smart contract for the CryptoKitties game. You can query the logs table for instances of this event here.

We can visualize the CryptoKitty pedigree, as shown here for accounts that own at least 10 CryptoKitties. Color indicates owner, while size indicates the PageRank reproductive fitness of each CryptoKitty: Analysis 2: Transaction Volumes and Transaction Networks There are many types of tokens being distributed on the Ethereum blockchain, and their patterns of distribution vary by type and also by time.

Find the answer here. Because the data, at a highly granular level, consists of a set of transfers between wallet addresses, we can also reason about the data using a directed graph data structure. In this graphic, nodes points represent wallet addresses on the Ethereum blockchain, and edges lines represent aggregate transfer of tokens between a pair of addresses.

Leveraging the power of BigQuery allows you to access the Ethereum blockchain via SQL and find meaningful insights rapidly. This data can also be easily exported to CSV, Avro, or JSON files and used for further analysis using graph databases, visualization tools, and machine learning frameworks. We focused on simplicity, consistency, and reliability when working on this project. Some interesting challenges and important design decisions will be covered in the following sections. Our architecture Ethereum ETL architecture diagram The ingestion pipeline is divided into two phases: export and load.

They are coordinated by Google Cloud Composer —a fully managed workflow orchestration service built on Apache Airflow. The exported files are then moved to Google Cloud Storage. In the second phase, the exported files are further processed, loaded into BigQuery , and finally verified. This algorithm is called Interpolation Search and takes O log log n time no typo there on uniformly distributed data. We implemented an improved version, which handles the worst case by probing estimation points differently , depending on whether the graph is convex or concave.

This handy command eliminates the need to store the latest synchronised block, making the workflow stateless. You can simply schedule the export tasks daily or hourly and get the block range to export based on the execution timestamp. They make the exporting process orders of magnitude faster by reducing the number of round trips to Ethereum nodes.

Unfortunately, batch requests are not supported by most Ethereum API clients as this feature requires language-specific architecture and needs to be thought through in the early stages of code design. As you can see, the exporting of receipts and logs runs significantly longer than all the other tasks. A receipt in Ethereum is an object containing information about the result of a transaction, such as its status, gas used, the set of logs created through execution of the transaction and the Bloom filter composed from information in those logs.

Even with request batching, this process is very slow. With the introduction of an API method that allows retrieving all receipts in a block, this issue will be solved. Here are feature requests for geth and parity that you should upvote if you want to support the project: geth , parity.

These logs are associated with the address of the contract and are incorporated into the blockchain. Every log contains a list of topics associated with it, which are used for index and search functionality. Exporting contracts Contracts in Ethereum are created with a special kind of transaction in which the receiver is set to 0x0.

The receipt for such a transaction contains a contractAddress field with the address of the created contract. Disassembling the bytecode with ethereum-dasm allows getting the initialization block of the contract and all PUSH4 opcodes in it. Below is an example output of ethereum-dasm : Screenshot of ethereum-dasm.

These 4-byte hashes serve as function IDs in transactions that are calls to contracts. For example, the ID of the transfer address,uint function is 0xacbb, so transactions invoking this method will contain 0xacbb as a prefix in the data field. Given that we can get all function IDs in a contract, we can tell if it conforms to some interface, such as ERC If all of the methods , defined in the standard, are present in a contract, we mark it as matching or belonging to type ERC The result of exporting contracts and the other export tasks in Airflow are the files in a Cloud Storage bucket, partitioned by day.