Google Bigquery

Introduction

Blockchain Analytics offers indexed blockchain data made available through BigQuery for easy analysis through SQL.

Blockchain Analytics offers you access to reliable data without the overhead of operating nodes or developing and maintaining an indexer. You can now query the full history of blocks, transactions, logs and receipts for Cronos.

By leveraging datasets in BigQuery, you can access blockchain data as easily as your internal data. By joining chain data with application data, you can get a complete picture of your users and your business.

How are these datasets different from the existing public dataset?

Like the existing public blockchain datasets, customers are not charged for storage of the data, only for querying the data based on BigQuery pricing.

Quickstart

1. Go to Cronos dataset and click on one of the samples.

2. You will get to the console and see the Cronos dataset on the left in the explorer

3. If you clicked on the sample you should get the BigQuery SQL code to query Which wallets had the most number of interactions with the Wrapped Cronos contract in the last 30 days? . Let's click the big RUN button. To start developing your own BigQuery SQL code, we refer to the following syntax. For the Cronos data schema we refer to the Google Cloud Cronos schema.

SELECT 
  t.from_address AS address, 
  CONCAT("https://cronoscan.com/address/", t.from_address) AS cronoscan_link,
  COUNT(t.from_address) AS num_transactions
FROM 
  `bigquery-public-data.goog_blockchain_cronos_mainnet_us.transactions` AS t
INNER JOIN
  bigquery-public-data.goog_blockchain_cronos_mainnet_us.blocks AS b
ON
  b.block_hash = t.block_hash
WHERE 
  t.to_address = LOWER("0x5C7F8A570d578ED84E63fdFA7b1eE72dEae1AE23") -- Wrapped CRO
AND
  b.block_timestamp > (CURRENT_TIMESTAMP() - INTERVAL 30 DAY)
GROUP BY 
  t.from_address
ORDER BY 
  COUNT(t.from_address) DESC
;

4. We can now query the results in the results tab below, further explore by exporting the results or visualizing in another tool such as Google sheets or Looker.

Row	address	cronoscan_link	num_transactions
1	0xb3c506d60d45abb917ee10a947749a098b497d3d	https://cronoscan.com/address/0xb3c506d60d45abb917ee10a947749a098b497d3d	370
2	0x693fb96fdda3c382fde7f43a622209c3dd028b98	https://cronoscan.com/address/0x693fb96fdda3c382fde7f43a622209c3dd028b98	347
3	0x6614d26064d762922c7bc7a00337713d5169ae7c	https://cronoscan.com/address/0x6614d26064d762922c7bc7a00337713d5169ae7c	137
4	0xce6aeeb31f00a5783c115a669e516f34d56512e4	https://cronoscan.com/address/0xce6aeeb31f00a5783c115a669e516f34d56512e4	120

Example queries

1. Latest indexed block

SELECT
  MIN(block_number) AS `First block`,
  MAX(block_number) AS `Newest block`,
  COUNT(1) AS `Total number of blocks`
FROM
  `bigquery-public-data.goog_blockchain_cronos_mainnet_us.blocks` AS t

Row	First block	Newest block	Total number of blocks
1	1	12134627	12134627

2. Daily transactions in the last 10 days

SELECT
  DATE(block_timestamp) AS date,
  COUNT(*) AS num_transactions
FROM
  `bigquery-public-data.goog_blockchain_cronos_mainnet_us.transactions`
WHERE
  block_timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 10 DAY)
GROUP BY
  1
ORDER BY
  1 DESC;

Row	date	num_transactions
1	2024-01-18	10250
2	2024-01-17	47747
3	2024-01-16	49717
4	2024-01-15	47099
5	2024-01-14	47051
6	2024-01-13	43926
7	2024-01-12	50448
8	2024-01-11	60904
9	2024-01-10	61774
10	2024-01-09	54521
11	2024-01-08	44194

3. View the blocks with largest CRO value transfer

SELECT block_hash, SUM(value.bignumeric_value / 1000000000000000000) value_total
FROM `bigquery-public-data.goog_blockchain_cronos_mainnet_us.transactions`
  JOIN `bigquery-public-data.goog_blockchain_cronos_mainnet_us.receipts` USING (block_hash, transaction_hash)
WHERE status = 1
GROUP BY block_hash
ORDER BY value_total DESC
LIMIT 5

Row	block_hash	value_total
1	0xd2fb7e0178b41b8a4226845b5f2e252eaded16018195bd8d9b0a19696205dbd3	200002596.616738410135301463
2	0xce79ec24ed1f3080b50980aacb9200a1e7bf25e9b382df13be2070d1d8d03142	173167791.450760254782540311
3	0x58d5a125a6950acac5664c8eeb285b2457563c47f858aed85c4c6d28609c10eb	150004978.49285843
4	0x402d0047c5e001a220b200c2ebeb8adfeddf4c5276972b586c3489b8e61d7d20	150000000
5	0xa1158b002a13cecc0a6a2061e71c395e0f1310812da26cc77c598d283571e485	129150494

4. Top 10 wallets by number of transactions

SELECT 
  from_address, 
  COUNT(*) AS num_transactions 
FROM `bigquery-public-data.goog_blockchain_cronos_mainnet_us.transactions`
GROUP BY from_address
ORDER BY num_transactions DESC
LIMIT 10;

Row	from_address	num_transactions
1	0xc9219731adfa70645be14cd5d30507266f2092c5	3435654
2	0xae45a8240147e6179ec7c9f92c5a18f9a97b3fca	610937
3	0xd166bcf1d581bb25ab597672ae8a4a02441d2b39	579612
4	0x95d49a8a2d69b2a2de4a00655d05ee39f9c41108	520301
5	0x71f0cdb17454ad7eeb7e26242292fe0e0189645a	355649
6	0xb3c506d60d45abb917ee10a947749a098b497d3d	321307
7	0x9b6e6035998a84bf2d42781752707087fe8229ed	309942
8	0x227f6757289a86c13eee2e91c2e6eb03f2ed11a6	294599
9	0x6614d26064d762922c7bc7a00337713d5169ae7c	267727
10	0x3936530e2f41df21889067ae35aa81ffbd68aeef	253452

5. All USDT activity

-- UDF for easier string manipulation.
CREATE TEMP FUNCTION ParseSubStr(hexStr STRING, startIndex INT64, endIndex INT64)
RETURNS STRING
LANGUAGE js
AS r"""
  if (hexStr.length < 1) {
    return hexStr;
  }
  return hexStr.substring(startIndex, endIndex);
""";

-- UDF to convert hex to decimal.
CREATE TEMP FUNCTION HexToDecimal(hexStr STRING)
RETURNS INT64
LANGUAGE js
AS r"""
  return parseInt(hexStr, 16);
""";

SELECT
  t.transaction_hash,
  t.from_address AS from_address,
  CONCAT("0x", ParseSubStr(l.topics[OFFSET(2)], 26, LENGTH(l.topics[OFFSET(2)]))) AS to_address,
  (HexToDecimal(l.data) / 1000000) AS usdt_transfer_amount
FROM
  `bigquery-public-data.goog_blockchain_cronos_mainnet_us.transactions` AS t
INNER JOIN
  `bigquery-public-data.goog_blockchain_cronos_mainnet_us.logs` AS l
ON
  l.transaction_hash = t.transaction_hash
WHERE
  t.to_address = LOWER("0x66e428c3f67a68878562e79a0234c1f83c208770") -- USDT
AND
  ARRAY_LENGTH(l.topics) > 0
AND
  -- Transfer(address indexed src, address indexed dst, uint wad)
  l.topics[OFFSET(0)] = LOWER("0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef")
;

Row	transaction_hash	from_address	to_address	usdt_transfer_amount
1	0x27f0439e4c557cfa4c5ffeb77bd53d39bd4380da0e70b0808731c6c6c570eb85	0x4ccb4f2bcb1f2808a3d326af1cc01a99c8c9c15d	0x6ab8a9861717631d7300d6ad88e77b4010acce11	36.26307
2	0x4128109503cd6b8e69a7ae8655dad22fd7a9a33d7ec526f5cc14351da55b1458	0xe330472d0398619c447bd5943e38fc24dc42d0b1	0x8995909dc0960fc9c75b6031d683124a4016825b	250.0
3	0x87ab0dad4c0e87bcb547ab448ea321d9606722e67702fc86b20b9e86876c81ad	0xcd1332b5cabdda8425a33a615399e1a0a17a2938	0x480468c2d8487429a096ef2bc0b58666b19ed291	10.0
4	0x91dd6b1b478c60d3f6aea8c88f0aa23d327bce3f22a796084f698e768513332a	0xe2ee00deb8d9e83e575e844610d8d864bc370066	0x56578a2c83b5bbac303c702e4c536b8a3e623ecf	1000.0
5	0x0406ad79cfb31ae5d1427a4d649d6eb78687dd4fff6d141d62e9d1d7b673b056	0xc6cf10c2379ec80aef796b6469230104aadd89c0	0x8995909dc0960fc9c75b6031d683124a4016825b	4803.813327