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. 1.
    Go to Cronos dataset and click on one of the samples.
  2. 2.
    You will get to the console and see the Cronos dataset on the left in the explorer
  1. 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
;
  1. 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. 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
Text
1
1
12134627
12134627
  1. 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
  1. 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
  1. 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
  1. 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