def yarn_site_xml_defaults(workdir, node_info):
'''
Default entries for the yarn-site.xml config file.
'''
mem_dflts = memory_defaults(node_info)
ncores = node_info['cores']
max_alloc = round_mb(mem_dflts.ram_per_container *
mem_dflts.num_containers)
min_alloc = round_mb(mem_dflts.ram_per_container)
dflts = {
'yarn.nodemanager.aux-services': 'mapreduce_shuffle',
'yarn.scheduler.maximum-allocation-mb': max_alloc,
'yarn.scheduler.minimum-allocation-mb': min_alloc,
'yarn.nodemanager.resource.memory-mb': max_alloc,
'yarn.nodemanager.vmem-check-enabled': 'false',
'yarn.nodemanager.vmem-pmem-ratio': 2.1,
'yarn.nodemanager.hostname': '$dataname',
'yarn.nodemanager.webapp.address': '$hostaddress:8042',
'yarn.resourcemanager.hostname': '$masterdataname',
'yarn.resourcemanager.webapp.address': '$masterhostaddress:8088',
'yarn.resourcemanager.webapp.https.address': '$masterhostaddress:8090',
'yarn.resourcemanager.scheduler.class':
'org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler',
'yarn.scheduler.capacity.allocation.file': 'capacity-scheduler.xml',
'yarn.scheduler.maximum-allocation-vcores': str(ncores),
'yarn.scheduler.minimum-allocation-vcores': '1',
'yarn.nodemanager.resource.cpu-vcores': str(ncores),
}
return dflts
def yarn_site_xml_defaults(workdir, node_info):
'''
Default entries for the yarn-site.xml config file.
'''
mem_dflts = memory_defaults(node_info)
ncores = node_info['cores']
max_alloc = round_mb(mem_dflts.ram_per_container * mem_dflts.num_containers)
min_alloc = round_mb(mem_dflts.ram_per_container)
dflts = {
'yarn.nodemanager.aux-services': 'mapreduce_shuffle',
'yarn.scheduler.maximum-allocation-mb': max_alloc,
'yarn.scheduler.minimum-allocation-mb': min_alloc,
'yarn.nodemanager.resource.memory-mb': max_alloc,
'yarn.nodemanager.vmem-check-enabled':'false',
'yarn.nodemanager.vmem-pmem-ratio': 2.1,
'yarn.nodemanager.hostname': '$dataname',
'yarn.nodemanager.webapp.address': '$hostaddress:8042',
'yarn.resourcemanager.hostname': '$masterdataname',
'yarn.resourcemanager.webapp.address': '$masterhostaddress:8088',
'yarn.resourcemanager.webapp.https.address': '$masterhostaddress:8090',
'yarn.resourcemanager.scheduler.class': 'org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler',
'yarn.scheduler.capacity.allocation.file': 'capacity-scheduler.xml',
'yarn.scheduler.maximum-allocation-vcores': str(ncores),
'yarn.scheduler.minimum-allocation-vcores': '1',
'yarn.nodemanager.resource.cpu-vcores': str(ncores),
}
return dflts
def mapred_site_xml_defaults(workdir, node_info):
'''
Default entries for the mapred-site.xml config file.
'''
mem_dflts = memory_defaults(node_info)
java_map_mem = format_memory(0.8 * mem_dflts.ram_per_container,
round_val=True)
java_reduce_mem = format_memory(0.8 * 2 * mem_dflts.ram_per_container,
round_val=True)
# In my tests, Yarn gets shirty if I try to run a job and these values are set to
# more then 8g:
map_memory = round_mb(mem_dflts.ram_per_container)
reduce_memory = round_mb(2 * mem_dflts.ram_per_container)
dflts = {
'mapreduce.framework.name':
'yarn',
'mapreduce.map.java.opts':
'-Xmx%s' % java_map_mem,
'mapreduce.map.memory.mb':
map_memory,
'mapreduce.reduce.java.opts':
'-Xmx%s' % java_reduce_mem,
'mapreduce.reduce.memory.mb':
reduce_memory,
# io.sort.mb can't be > 2047mb
'mapreduce.task.io.sort.mb':
min(int(0.4 * map_memory), 2047),
'yarn.app.mapreduce.am.staging-dir':
'$localworkdir/tmp/hadoop-yarn/staging',
}
return dflts
def yarn_site_xml_defaults(workdir, node_info):
"""
Default entries for the yarn-site.xml config file.
"""
mem_dflts = memory_defaults(node_info)
ncores = node_info["cores"]
max_alloc = round_mb(mem_dflts.ram_per_container * mem_dflts.num_containers)
min_alloc = round_mb(mem_dflts.ram_per_container)
dflts = {
"yarn.nodemanager.aux-services": "mapreduce_shuffle",
"yarn.scheduler.maximum-allocation-mb": max_alloc,
"yarn.scheduler.minimum-allocation-mb": min_alloc,
"yarn.nodemanager.resource.memory-mb": max_alloc,
"yarn.nodemanager.vmem-check-enabled": "false",
"yarn.nodemanager.vmem-pmem-ratio": 2.1,
"yarn.nodemanager.hostname": "$dataname",
"yarn.nodemanager.webapp.address": "$hostaddress:8042",
"yarn.resourcemanager.hostname": "$masterdataname",
"yarn.resourcemanager.webapp.address": "$masterhostaddress:8088",
"yarn.resourcemanager.webapp.https.address": "$masterhostaddress:8090",
"yarn.resourcemanager.scheduler.class": "org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler",
"yarn.scheduler.capacity.allocation.file": "capacity-scheduler.xml",
"yarn.scheduler.maximum-allocation-vcores": str(ncores),
"yarn.scheduler.minimum-allocation-vcores": "1",
"yarn.nodemanager.resource.cpu-vcores": str(ncores),
}
return dflts
def test_mapred_site_xml_defaults(self):
node = dict(fqdn='hosty.domain.be', network='ib0', pid=1234,
cores=24, totalcores=24, usablecores=range(24), num_nodes=1,
memory=dict(meminfo=dict(memtotal=68719476736), ulimit='unlimited'))
d = hca.mapred_site_xml_defaults('/', node)
self.assertEqual(len(d), 7)
# Capped at 8g
self.assertEqual(d['mapreduce.map.memory.mb'], hcc.round_mb(hcc.parse_memory('2G')))
self.assertEqual(d['mapreduce.reduce.memory.mb'], hcc.round_mb(hcc.parse_memory('4G')))
def test_mapred_site_xml_defaults(self):
node = dict(fqdn='hosty.domain.be', network='ib0', pid=1234,
cores=24, totalcores=24, usablecores=range(24), num_nodes=1,
memory=dict(meminfo=dict(memtotal=68719476736), ulimit='unlimited'))
d = hca.mapred_site_xml_defaults('/', node)
self.assertEqual(len(d), 7)
# Capped at 8g
self.assertEqual(d['mapreduce.map.memory.mb'], hcc.round_mb(hcc.parse_memory('2G')))
self.assertEqual(d['mapreduce.reduce.memory.mb'], hcc.round_mb(hcc.parse_memory('4G')))
def test_mapred_site_xml_defaults(self):
'''Test mapred defaults; note: only using 4 from 24 cores.'''
node = dict(fqdn='hosty.domain.be', network='ib0', pid=1234,
cores=4, totalcores=24, usablecores=[0, 1, 2, 3], num_nodes=1,
memory=dict(meminfo=dict(memtotal=68719476736), ulimit='unlimited'))
d = hca.mapred_site_xml_defaults('/', node)
self.assertEqual(len(d), 9)
self.assertEqual(d['hadoop.ln.cmd'], '/bin/ln')
self.assertEqual(d['lustre.dir'], '$workdir')
self.assertEqual(d['mapreduce.map.memory.mb'], hcc.round_mb(hcc.parse_memory('1G')))
self.assertEqual(d['mapreduce.reduce.memory.mb'], hcc.round_mb(hcc.parse_memory('2G')))
def test_mapred_site_xml_defaults(self):
'''Test mapred defaults; note: only using 4 from 24 cores.'''
node = dict(fqdn='hosty.domain.be', network='ib0', pid=1234,
cores=4, totalcores=24, usablecores=[0, 1, 2, 3], num_nodes=1,
memory=dict(meminfo=dict(memtotal=68719476736), ulimit='unlimited'))
d = hca.mapred_site_xml_defaults('/', node)
self.assertEqual(len(d), 9)
self.assertEqual(d['hadoop.ln.cmd'], '/bin/ln')
self.assertEqual(d['lustre.dir'], '$workdir')
self.assertEqual(d['mapreduce.map.memory.mb'], hcc.round_mb(hcc.parse_memory('1G')))
self.assertEqual(d['mapreduce.reduce.memory.mb'], hcc.round_mb(hcc.parse_memory('2G')))
def spark_defaults(_, node_info):
'''
Generate spark defaults so spark uses all the resources that yarn is able to
provide.
Defaults here are based on Cloudera's recommendations here:
http://blog.cloudera.com/blog/2015/03/how-to-tune-your-apache-spark-jobs-part-2/
We use 2 cores per executor based on discussion found here:
http://stackoverflow.com/questions/24622108/apache-spark-the-number-of-cores-vs-the-number-of-executors
'''
memory_defaults = hcah.memory_defaults(node_info)
num_nodes = node_info['num_nodes']
cores_per_executor = min(2, node_info['cores'])
instances_per_node = node_info['cores'] / cores_per_executor
# -1 because we want one less executor instance on the application master
# If we have only one node then we don't expect the driver to be very busy, so
# we can give the executors more memory.
instances = max((num_nodes * instances_per_node) - 1, 1)
memory = hcac.round_mb(memory_defaults.available_memory / instances_per_node)
dflts = {
'spark.executor.cores': cores_per_executor,
'spark.executor.instances': instances,
'spark.executor.memory': str(memory) + 'M',
'spark.local.dir': tempfile.gettempdir(),
}
return dflts
def test_mapred_site_xml_defaults(self):
node = dict(
fqdn="hosty.domain.be",
network="ib0",
pid=1234,
cores=24,
totalcores=24,
usablecores=range(24),
num_nodes=1,
memory=dict(meminfo=dict(memtotal=68719476736), ulimit="unlimited"),
)
d = hca.mapred_site_xml_defaults("/", node)
self.assertEqual(len(d), 7)
# Capped at 8g
self.assertEqual(d["mapreduce.map.memory.mb"], hcc.round_mb(hcc.parse_memory("2G")))
self.assertEqual(d["mapreduce.reduce.memory.mb"], hcc.round_mb(hcc.parse_memory("4G")))
def test_yarn_site_xml_defaults(self):
node = dict(fqdn='hosty.domain.be', network='ib0', pid=1234,
cores=24, totalcores=24, usablecores=range(24), num_nodes=1,
memory=dict(meminfo=dict(memtotal=68719476736), ulimit='unlimited'))
d = hca.yarn_site_xml_defaults('/', node)
self.assertEqual(len(d), 16)
self.assertEqual(d['yarn.nodemanager.resource.memory-mb'], hcc.round_mb(hcc.parse_memory('56G')))
self.assertEqual(d['yarn.resourcemanager.webapp.address'], '$masterhostaddress:8088')
self.assertEqual(d['yarn.resourcemanager.webapp.https.address'], '$masterhostaddress:8090')
self.assertEqual(d['yarn.nodemanager.hostname'], '$dataname')
self.assertEqual(d['yarn.nodemanager.webapp.address'], '$hostaddress:8042')
self.assertEqual(d['yarn.scheduler.minimum-allocation-mb'], hcc.round_mb(hcc.parse_memory('2G')))
self.assertEqual(d['yarn.scheduler.maximum-allocation-mb'], hcc.round_mb(hcc.parse_memory('56G')))
self.assertEqual(d['yarn.scheduler.maximum-allocation-vcores'], '24')
self.assertEqual(d['yarn.scheduler.minimum-allocation-vcores'], '1')
self.assertEqual(d['yarn.nodemanager.resource.cpu-vcores'], '24')
def test_yarn_site_xml_defaults(self):
node = dict(fqdn='hosty.domain.be', network='ib0', pid=1234,
cores=24, totalcores=24, usablecores=range(24), num_nodes=1,
memory=dict(meminfo=dict(memtotal=68719476736), ulimit='unlimited'))
d = hca.yarn_site_xml_defaults('/', node)
self.assertEqual(len(d), 16)
self.assertEqual(d['yarn.nodemanager.resource.memory-mb'], hcc.round_mb(hcc.parse_memory('56G')))
self.assertEqual(d['yarn.resourcemanager.webapp.address'], '$masterhostaddress:8088')
self.assertEqual(d['yarn.resourcemanager.webapp.https.address'], '$masterhostaddress:8090')
self.assertEqual(d['yarn.nodemanager.hostname'], '$dataname')
self.assertEqual(d['yarn.nodemanager.webapp.address'], '$hostaddress:8042')
self.assertEqual(d['yarn.scheduler.minimum-allocation-mb'], hcc.round_mb(hcc.parse_memory('2G')))
self.assertEqual(d['yarn.scheduler.maximum-allocation-mb'], hcc.round_mb(hcc.parse_memory('56G')))
self.assertEqual(d['yarn.scheduler.maximum-allocation-vcores'], '24')
self.assertEqual(d['yarn.scheduler.minimum-allocation-vcores'], '1')
self.assertEqual(d['yarn.nodemanager.resource.cpu-vcores'], '24')
def spark_defaults(_, node_info):
'''
Generate spark defaults so spark uses all the resources that yarn is able to
provide.
Defaults here are based on Cloudera's recommendations here:
http://blog.cloudera.com/blog/2015/03/how-to-tune-your-apache-spark-jobs-part-2/
We use 2 cores per executor based on discussion found here:
http://stackoverflow.com/questions/24622108/apache-spark-the-number-of-cores-vs-the-number-of-executors
'''
memory_defaults = hcah.memory_defaults(node_info)
num_nodes = node_info['num_nodes']
cores_per_executor = min(2, node_info['cores'])
instances_per_node = node_info['cores'] / cores_per_executor
# -1 because we want one less executor instance on the application master
# If we have only one node then we don't expect the driver to be very busy, so
# we can give the executors more memory.
instances = max((num_nodes * instances_per_node) - 1, 1)
memory = hcac.round_mb(memory_defaults.available_memory / instances_per_node)
dflts = {
'spark.executor.cores': cores_per_executor,
'spark.executor.instances': instances,
'spark.executor.memory': str(memory) + 'M',
'spark.local.dir': tempfile.gettempdir(),
}
return dflts
def mapred_site_xml_defaults(workdir, node_info):
'''
Default entries for the mapred-site.xml config file.
'''
mem_dflts = memory_defaults(node_info)
java_map_mem = format_memory(0.8 * mem_dflts.ram_per_container, round_val=True)
java_reduce_mem = format_memory(0.8 * 2 * mem_dflts.ram_per_container, round_val=True)
# In my tests, Yarn gets shirty if I try to run a job and these values are set to
# more then 8g:
map_memory = round_mb(mem_dflts.ram_per_container)
reduce_memory = round_mb(2 * mem_dflts.ram_per_container)
dflts = {
'mapreduce.framework.name': 'yarn',
'mapreduce.map.java.opts': '-Xmx%s' % java_map_mem,
'mapreduce.map.memory.mb': map_memory,
'mapreduce.reduce.java.opts': '-Xmx%s' % java_reduce_mem,
'mapreduce.reduce.memory.mb': reduce_memory,
# io.sort.mb can't be > 2047mb
'mapreduce.task.io.sort.mb': min(int(0.4 * map_memory), 2047),
'yarn.app.mapreduce.am.staging-dir': '$localworkdir/tmp/hadoop-yarn/staging',
}
return dflts
def test_yarn_site_xml_defaults(self):
node = dict(
fqdn="hosty.domain.be",
network="ib0",
pid=1234,
cores=24,
totalcores=24,
usablecores=range(24),
num_nodes=1,
memory=dict(meminfo=dict(memtotal=68719476736), ulimit="unlimited"),
)
d = hca.yarn_site_xml_defaults("/", node)
self.assertEqual(len(d), 16)
self.assertEqual(d["yarn.nodemanager.resource.memory-mb"], hcc.round_mb(hcc.parse_memory("56G")))
self.assertEqual(d["yarn.resourcemanager.webapp.address"], "$masterhostaddress:8088")
self.assertEqual(d["yarn.resourcemanager.webapp.https.address"], "$masterhostaddress:8090")
self.assertEqual(d["yarn.nodemanager.hostname"], "$dataname")
self.assertEqual(d["yarn.nodemanager.webapp.address"], "$hostaddress:8042")
self.assertEqual(d["yarn.scheduler.minimum-allocation-mb"], hcc.round_mb(hcc.parse_memory("2G")))
self.assertEqual(d["yarn.scheduler.maximum-allocation-mb"], hcc.round_mb(hcc.parse_memory("56G")))
self.assertEqual(d["yarn.scheduler.maximum-allocation-vcores"], "24")
self.assertEqual(d["yarn.scheduler.minimum-allocation-vcores"], "1")
self.assertEqual(d["yarn.nodemanager.resource.cpu-vcores"], "24")
def test_round_mb(self):
mb = 1024
self.assertEqual(hcc.round_mb(512 * (1024**3)), 512 * mb)
self.assertEqual(hcc.round_mb(64 * (1024**3)), 64 * mb)
self.assertEqual(hcc.round_mb(32 * (1024**3)), 32 * mb)
self.assertEqual(hcc.round_mb(32 * (1024**3) + 100*(1024**2)), 32 * mb)
self.assertEqual(hcc.round_mb(32 * (1024**3) - 100*(1024**2)), 32 * mb - 1024)
self.assertEqual(hcc.round_mb(16 * (1024**3)), 16 * mb)
self.assertEqual(hcc.round_mb(8 * (1024**3)), 8 * mb)
self.assertEqual(hcc.round_mb(4 * (1024**3)), 4 * mb)
self.assertEqual(hcc.round_mb(3 * (1024**3)), 3 * mb)
self.assertEqual(hcc.round_mb(2 * (1024**3)), 2 * mb)
self.assertEqual(hcc.round_mb(1 * (1024**3)), 1 * mb)
def test_round_mb(self):
mb = 1024
self.assertEqual(hcc.round_mb(512 * (1024**3)), 512 * mb)
self.assertEqual(hcc.round_mb(64 * (1024**3)), 64 * mb)
self.assertEqual(hcc.round_mb(32 * (1024**3)), 32 * mb)
self.assertEqual(hcc.round_mb(32 * (1024**3) + 100 * (1024**2)),
32 * mb)
self.assertEqual(hcc.round_mb(32 * (1024**3) - 100 * (1024**2)),
32 * mb - 1024)
self.assertEqual(hcc.round_mb(16 * (1024**3)), 16 * mb)
self.assertEqual(hcc.round_mb(8 * (1024**3)), 8 * mb)
self.assertEqual(hcc.round_mb(4 * (1024**3)), 4 * mb)
self.assertEqual(hcc.round_mb(3 * (1024**3)), 3 * mb)
self.assertEqual(hcc.round_mb(2 * (1024**3)), 2 * mb)
self.assertEqual(hcc.round_mb(1 * (1024**3)), 1 * mb)
