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Data Mining

The block number hash of the block the address of the beneficiary to whom the mining rewards were given integer of the difficulty for this block the size of this block in bytes

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

DATASET SCHEMA - BLOCKS

number: The block number

hash: Hash of the block

miner: The address of the beneficiary to whom the mining rewards were given

difficulty: Integer of the difficulty for this block

size: The size of this block in bytes

gas_limit: The maximum gas allowed in this block

gas_used: The total used gas by all transactions in this block

timestamp: The timestamp for when the block was collated

transaction_count: The number of transactions in the block

DATASET SCHEMA - TRANSACTIONS

block_number: Block number where this transaction was in

from_address: Address of the sender

to_address: Address of the receiver. null when it is a contract creation transaction

value: Value transferred in Wei (the smallest denomination of ether)

gas: Gas provided by the sender

gas_price : Gas price provided by the sender in Wei

block_timestamp: Timestamp the associated block was registered at (effectively timestamp of the transaction)

DATASET SCHEMA - CONTRACTS

address: Address of the contract

is_erc20: Whether this contract is an ERC20 contract

is_erc721: Whether this contract is an ERC721 contract

block_number: Block number where this contract was created

DATASET SCHEMA - SCAMS.JSON

id: Unique ID for the reported scam

name: Name of the Scam

url: Hosting URL

coin: Currency the scam is attempting to gain

category: Category of scam - Phishing, Ransomware, Trust Trade, etc.

subcategory: Subdivisions of Category

description: Description of the scam provided by the reporter and datasource

addresses: List of known addresses associated with the scam

reporter: User/company who reported the scam first

ip: IP address of the reporter

status: If the scam is currently active, inactive or has been taken offline

0x11c058c3efbf53939fb6872b09a2b5cf2410a1e2c3f3c867664e43a626d878c0: {

    id: 81,

    name: "myetherwallet.us",

    url: "http://myetherwallet.us",

    coin: "ETH",

    category: "Phishing",

    subcategory: "MyEtherWallet",

    description: "did not 404.,MEW Deployed",

    addresses: [

        "0x11c058c3efbf53939fb6872b09a2b5cf2410a1e2c3f3c867664e43a626d878c0",

        "0x2dfe2e0522cc1f050edcc7a05213bb55bbb36884ec9468fc39eccc013c65b5e4",

        "0x1c6e3348a7ea72ffe6a384e51bd1f36ac1bcb4264f461889a318a3bb2251bf19"

    ],

    reporter: "MyCrypto",

    ip: "198.54.117.200",

    nameservers: [

        "dns102.registrar-servers.com",

        "dns101.registrar-servers.com"

    ],

    status: "Offline"

},

Evaluate the top 10 smart contracts by total Ether received. An outline of the subtasks required to extract this information is provided below, focusing on a MRJob based approach. This is, however, only one possibility, with several other viable ways of completing this assignment.

JOB 1 - INITIAL AGGREGATION

To workout which services are the most popular, you will first have to aggregate transactions to see how much each address within the user space has been involved in. You will want to aggregate value for addresses in the to_address field. 

JOB 2 - JOINING TRANSACTIONS/CONTRACTS AND FILTERING

Once you have obtained this aggregate of the transactions, the next step is to perform a repartition join between this aggregate and contracts. You will want to join the to_address field from the output of Job 1 with the address field of contracts

Secondly, in the reducer, if the address for a given aggregate from Job 1 was not present within contracts this should be filtered out as it is a user address and not a smart contract.

JOB 3 - TOP TEN

Finally, the third job will take as input the now filtered address aggregates and sort these via a top ten reducer

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