def simulation_a1(n):
init_tx = simulator.init()
process(init_tx)
create_tx = simulator.create((init_tx['transaction']['outputs'][0],), None, (str(n),))
process(create_tx)
outputs = create_tx['transaction']['outputs']
transactions = [simulator.consume((output,)) for output in outputs]
process_many(transactions)
def simulation_a3(n):
init_tx = simulator.init()
dump(init_tx)
create_tx = simulator.create((init_tx['transaction']['outputs'][0],), None, (str(n),))
dump(create_tx)
outputs = create_tx['transaction']['outputs']
transactions = [simulator.consume((output,)) for output in outputs]
dump_many(transactions)
def simulation_batched(n,
inputs_per_tx,
batch_size=100,
batch_sleep=2,
nonce=True,
shards_per_tx=None,
num_shards=None,
co=False):
init_tx = simulator.init()
process(init_tx)
parameters = [
str(n * inputs_per_tx),
]
if nonce:
parameters.append(str(random.randint(1, 1000000)))
create_tx = simulator.create((init_tx['transaction']['outputs'][0], ),
None, parameters)
process(create_tx)
time.sleep(5)
outputs = create_tx['transaction']['outputs']
if shards_per_tx is not None:
outputs_map = {}
for shard in range(num_shards):
outputs_map[shard] = []
for output in outputs:
outputs_map[map_object_id_to_shard(
num_shards, output.object_id)].append(output)
transactions = []
try:
for i in range(0, len(outputs), inputs_per_tx):
if shards_per_tx is not None:
rand = random.randint(0, num_shards - shards_per_tx)
objects = []
for j in range(inputs_per_tx):
if shards_per_tx is None:
objects.append(outputs[i + j])
else:
shard_id = j % shards_per_tx
shard_id = shard_id + rand
objects.append(outputs_map[shard_id].pop())
if co:
transactions.append(simulator.consume_co(objects))
else:
transactions.append(simulator.consume(objects))
except IndexError:
pass
for i in range(0, len(transactions), batch_size):
dump_many(transactions[i:i + batch_size])
time.sleep(batch_sleep)
def simulation_b2(n, inputs_per_tx):
init_tx = simulator.init()
process(init_tx)
create_tx = simulator.create((init_tx['transaction']['outputs'][0],), None, (str(n),))
process(create_tx)
outputs = create_tx['transaction']['outputs']
transactions = []
for i in range(0, len(outputs), inputs_per_tx):
objects = []
for j in range(inputs_per_tx):
objects.append(outputs[i+j])
transactions.append(simulator.consume(objects))
dump_many(transactions)
def simulation_batched(n, inputs_per_tx, batch_size=100, batch_sleep=2, nonce=True):
init_tx = simulator.init()
process(init_tx)
parameters = [str(n*inputs_per_tx),]
if nonce:
parameters.append(str(random.randint(1,1000000)))
create_tx = simulator.create((init_tx['transaction']['outputs'][0],), None, parameters)
process(create_tx)
time.sleep(5)
outputs = create_tx['transaction']['outputs']
transactions = []
for i in range(0, len(outputs), inputs_per_tx):
objects = []
for j in range(inputs_per_tx):
objects.append(outputs[i+j])
transactions.append(simulator.consume(objects))
for i in range(0, len(transactions), batch_size):
dump_many(transactions[i:i+batch_size])
time.sleep(batch_sleep)
