mirror of
https://github.com/twitter/the-algorithm.git
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617c8c787d
Unified User Action (UUA) is a centralized, real-time stream of user actions on Twitter, consumed by various product, ML, and marketing teams. UUA makes sure all internal teams consume the uniformed user actions data in an accurate and fast way.
152 lines
4.0 KiB
Plaintext
152 lines
4.0 KiB
Plaintext
import os
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import itertools
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import subprocess
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import math
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SERVICE_NAME = 'uua-enricher'
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CPU_NUM = 3
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HEAP_SIZE = 6 * GB
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RAM_SIZE = 8 * GB
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DISK_SIZE = 3 * GB
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class Profile(Struct):
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package = Default(String, SERVICE_NAME)
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cmdline_flags = Default(String, '')
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log_level = Default(String, 'INFO')
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instances = Default(Integer, 10)
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kafka_bootstrap_servers = Default(String, '/s/kafka/bluebird-1:kafka-tls')
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resources = Resources(
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cpu = CPU_NUM,
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ram = RAM_SIZE,
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disk = DISK_SIZE
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)
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install = Packer.install(
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name = '{{profile.package}}',
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version = Workflows.package_version()
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)
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async_profiler_install = Packer.install(
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name = 'async-profiler',
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role = 'csl-perf',
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version = 'latest'
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)
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setup_jaas_config = Process(
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name = 'setup_jaas_config',
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cmdline = '''
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mkdir -p jaas_config
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echo "KafkaClient {
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com.sun.security.auth.module.Krb5LoginModule required
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principal=\\"discode@TWITTER.BIZ\\"
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useKeyTab=true
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storeKey=true
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keyTab=\\"/var/lib/tss/keys/fluffy/keytabs/client/discode.keytab\\"
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doNotPrompt=true;
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};" >> jaas_config/jaas.conf
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'''
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)
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main = JVMProcess(
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name = SERVICE_NAME,
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jvm = Java11(
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heap = HEAP_SIZE,
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extra_jvm_flags =
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'-Djava.net.preferIPv4Stack=true'
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' -XX:+UseNUMA'
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' -XX:+AggressiveOpts'
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' -XX:+PerfDisableSharedMem' # http://www.evanjones.ca/jvm-mmap-pause.html
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' -Dlog_level={{profile.log_level}}'
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' -Dlog.access.output=access.log'
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' -Dlog.service.output={{name}}.log'
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' -Djava.security.auth.login.config=jaas_config/jaas.conf'
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),
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arguments =
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'-jar {{name}}-bin.jar'
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' -admin.port=:{{thermos.ports[health]}}'
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' -kafka.bootstrap.servers={{profile.kafka_bootstrap_servers}}'
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' -kafka.application.id={{name}}.{{environment}}'
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' -kafka.application.num.instances={{instances}}' # Used for static partitioning
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' -kafka.application.server={{mesos.instance}}.{{name}}.{{environment}}.{{role}}.service.{{cluster}}.twitter.com:80'
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' -com.twitter.finatra.kafkastreams.config.principal={{role}}'
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' -thrift.client.id={{name}}.{{environment}}'
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' -service.identifier="{{role}}:{{name}}:{{environment}}:{{cluster}}"'
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' -local.cache.ttl.seconds=86400'
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' -local.cache.max.size=400000000'
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' {{profile.cmdline_flags}}',
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resources = resources
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)
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stats = Stats(
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library = 'metrics',
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port = 'admin'
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)
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job_template = Service(
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name = SERVICE_NAME,
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role = 'discode',
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instances = '{{profile.instances}}',
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contact = 'disco-data-eng@twitter.com',
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constraints = {'rack': 'limit:1', 'host': 'limit:1'},
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announce = Announcer(
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primary_port = 'health',
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portmap = {'aurora': 'health', 'admin': 'health'}
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),
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task = Task(
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resources = resources,
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name = SERVICE_NAME,
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processes = [async_profiler_install, install, setup_jaas_config, main, stats],
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constraints = order(async_profiler_install, install, setup_jaas_config, main)
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),
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health_check_config = HealthCheckConfig(
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initial_interval_secs = 100,
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interval_secs = 60,
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timeout_secs = 60,
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max_consecutive_failures = 4
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),
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update_config = UpdateConfig(
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batch_size = 50,
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watch_secs = 90,
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max_per_shard_failures = 3,
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max_total_failures = 0,
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rollback_on_failure = False
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)
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)
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PRODUCTION = Profile(
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)
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STAGING = Profile(
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package = SERVICE_NAME+'-staging',
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cmdline_flags = '',
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kafka_bootstrap_servers = '/s/kafka/custdevel:kafka-tls'
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)
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DEVEL = STAGING(
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log_level = 'DEBUG',
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)
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prod_job = job_template(
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tier = 'preferred',
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environment = 'prod',
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).bind(profile = PRODUCTION)
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staging_job = job_template(
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environment = 'staging'
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).bind(profile = STAGING)
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devel_job = job_template(
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environment = 'devel'
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).bind(profile = DEVEL)
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jobs = []
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for cluster in ['atla', 'pdxa']:
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jobs.append(prod_job(cluster = cluster))
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jobs.append(staging_job(cluster = cluster))
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jobs.append(devel_job(cluster = cluster))
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