def chain_measure_bps_and_tps(archival_node,
start_time,
end_time,
duration=None):
latest_block_hash = archival_node.get_status(
)['sync_info']['latest_block_hash']
curr_block = archival_node.get_block(latest_block_hash)['result']
curr_time = get_timestamp(curr_block)
if end_time is None:
end_time = curr_time
if start_time is None:
start_time = end_time - duration
# one entry per block, equal to the timestamp of that block
block_times = []
# one entry per block (because there is only one shard), equal to the number of transactions
tx_count = []
while curr_time > start_time:
if curr_time < end_time:
block_times.append(curr_time)
chunk_hash = curr_block['chunks'][0]['chunk_hash']
chunk = archival_node.get_chunk(chunk_hash)['result']
tx_count.append(len(chunk['transactions']))
prev_hash = curr_block['header']['prev_hash']
curr_block = archival_node.get_block(prev_hash)['result']
curr_time = get_timestamp(curr_block)
block_times.reverse()
tx_count.reverse()
tx_cumulative = data.compute_cumulative(tx_count)
bps = data.compute_rate(block_times)
tps_fit = data.linear_regression(block_times, tx_cumulative)
return {'bps': bps, 'tps': tps_fit['slope']}
def chain_measure_bps_and_tps(archival_node,
start_time,
end_time,
duration=None):
latest_block_hash = archival_node.get_latest_block().hash
curr_block = archival_node.get_block(latest_block_hash)['result']
curr_time = get_timestamp(curr_block)
if end_time is None:
end_time = curr_time
if start_time is None:
start_time = end_time - duration
logger.info(
f'Measuring BPS and TPS in the time range {start_time} to {end_time}')
# One entry per block, equal to the timestamp of that block.
block_times = []
# One entry per block, containing the count of transactions in all chunks of the block.
tx_count = []
block_counter = 0
while curr_time > start_time:
if curr_time < end_time:
block_times.append(curr_time)
gas_per_chunk = []
for chunk in curr_block['chunks']:
gas_per_chunk.append(chunk['gas_used'] * 1e-12)
gas_block = sum(gas_per_chunk)
tx_per_chunk = [None] * len(curr_block['chunks'])
pmap(
lambda i: get_chunk_txn(i, curr_block['chunks'], archival_node,
tx_per_chunk),
range(len(curr_block['chunks'])))
txs = sum(tx_per_chunk)
tx_count.append(txs)
logger.info(
f'Processed block at time {curr_time} height #{curr_block["header"]["height"]}, # txs in a block: {txs}, per chunk: {tx_per_chunk}, gas in block: {gas_block}, gas per chunk: {gas_per_chunk}'
)
prev_hash = curr_block['header']['prev_hash']
curr_block = archival_node.get_block(prev_hash)['result']
curr_time = get_timestamp(curr_block)
block_times.reverse()
tx_count.reverse()
assert block_times
tx_cumulative = data.compute_cumulative(tx_count)
bps = data.compute_rate(block_times)
tps_fit = data.linear_regression(block_times, tx_cumulative)
logger.info(
f'Num blocks: {len(block_times)}, num transactions: {len(tx_count)}, bps: {bps}, tps_fit: {tps_fit}'
)
return {'bps': bps, 'tps': tps_fit['slope']}
def measure_tps_bps(nodes, tx_filename):
input_tx_events = mocknet.get_tx_events(nodes, tx_filename)
# drop first and last 5% of events to avoid edges of test
n = int(0.05 * len(input_tx_events))
input_tx_events = input_tx_events[n:-n]
input_tps = data.compute_rate(input_tx_events)
measurement = mocknet.chain_measure_bps_and_tps(nodes[-1],
input_tx_events[0],
input_tx_events[-1])
result = {
'bps': measurement['bps'],
'in_tps': input_tps,
'out_tps': measurement['tps']
}
logger.info(f'{result}')
return result