之前有写过对接 kafka 的教程，kinesis 作为 aws 的 MQ 中间件，在国外公司以及使用 aws 体系的公司有非常重要的地位。
因为对比 kafka 使用 aws 提供的 kinesis 不仅可以大幅节约成本，而且可以非常方便的通过调整 shards 去平衡处理能力和费用。(shards 的概念有点类似 kafka 里面的 partitiosn 但是 shards 不仅可以增加，也可以减少！所以更像 kinesis 的一个吞吐单位)
Producer:
不同于 kafka 或者 msk (aws 托管 kafka) ，kinesis 通过 key、region 、data stream 的名字来指明连接的对象。中间没有 ip 地址的出现，所以会让人觉得很懵逼
在配置 maxwell 的时候我们除了需要配置 config.properties 将我们使用的 backend 调整为 kinesis (默认是使用 kafka)，还需要额外配置一个叫 kinesis-producer-library.properties 的配置文件。当我们在 config.properties 中配置使用的 backend 是 kinesis 的时候，maxwell 就会读取 kinesis-producer-library.properties 的配置文件。
我会在下面贴两个已经成功运行的配置文件。
config.properties
# maxwell config
# tl;dr config
#log_level=info
#producer=kafka
#kafka.bootstrap.servers=localhost:9092
# mysql login info
#host=localhost
#user=maxwell
#password=maxwell
# *** general ***
# choose where to produce data to. stdout|file|kafka|kinesis|pubsub|sqs|rabbitmq|redis
producer=kinesis
# set the log level. note that you can configure things further in log4j2.xml
#log_level=INFO # [DEBUG, INFO, WARN, ERROR]
log_level=INFO
# if set, maxwell will look up the scoped environment variables, strip off the prefix and inject the configs
#env_config_prefix=MAXWELL_
# *** mysql ***
# mysql host to connect to
host=db
# mysql port to connect to
port=3306
# mysql user to connect as. This user must have REPLICATION SLAVE permissions,
# as well as full access to the `maxwell` (or schema_database) database
user=maxwell
# mysql password
password=xxxxx
# options to pass into the jdbc connection, given as opt=val&opt2=val2
#jdbc_options=opt1=100&opt2=hello
# name of the mysql database where maxwell keeps its own state
#schema_database=maxwell
# whether to use GTID or not for positioning
#gtid_mode=true
# maxwell will capture an initial "base" schema containing all table and column information,
# and then keep delta-updates on top of that schema. If you have an inordinate amount of DDL changes,
# the table containing delta changes will grow unbounded (and possibly too large) over time. If you
# enable this option Maxwell will periodically compact its tables.
#max_schemas=10000
# SSL/TLS options
# To use VERIFY_CA or VERIFY_IDENTITY, you must set the trust store with Java opts:
# -Djavax.net.ssl.trustStore=<truststore> -Djavax.net.ssl.trustStorePassword=<password>
# or import the MySQL cert into the global Java cacerts.
# MODE must be one of DISABLED, PREFERRED, REQUIRED, VERIFY_CA, or VERIFY_IDENTITY
#
# turns on ssl for the maxwell-store connection, other connections inherit this setting unless specified
#ssl=DISABLED
# for binlog-connector
#replication_ssl=DISABLED
# for the schema-capture connection, if used
#schema_ssl=DISABLED
# maxwell can optionally replicate from a different server than where it stores
# schema and binlog position info. Specify that different server here:
#replication_host=other
#replication_user=username
#replication_password=password
#replication_port=3306
# This may be useful when using MaxScale's binlog mirroring host.
# Specifies that Maxwell should capture schema from a different server than
# it replicates from:
#schema_host=other
#schema_user=username
#schema_password=password
#schema_port=3306
# *** output format ***
# records include binlog position (default false)
#output_binlog_position=true
# records include a gtid string (default false)
#output_gtid_position=true
# records include fields with null values (default true). If this is false,
# fields where the value is null will be omitted entirely from output.
#output_nulls=true
# records include server_id (default false)
#output_server_id=true
# records include thread_id (default false)
#output_thread_id=true
# records include schema_id (default false)
#output_schema_id=true
# records include row query, binlog option "binlog_rows_query_log_events" must be enabled" (default false)
#output_row_query=true
# DML records include list of values that make up a row's primary key (default false)
#output_primary_keys=true
# DML records include list of columns that make up a row's primary key (default false)
#output_primary_key_columns=true
# records include commit and xid (default true)
#output_commit_info=true
# This controls whether maxwell will output JSON information containing
# DDL (ALTER/CREATE TABLE/ETC) infromation. (default: false)
# See also: ddl_kafka_topic
#output_ddl=true
# turns underscore naming style of fields to camel case style in JSON output
# default is none, which means the field name in JSON is the exact name in MySQL table
#output_naming_strategy=underscore_to_camelcase
# *** kafka ***
# list of kafka brokers
#kafka.bootstrap.servers=hosta:9092,hostb:9092
# kafka topic to write to
# this can be static, e.g. 'maxwell', or dynamic, e.g. namespace_%{database}_%{table}
# in the latter case 'database' and 'table' will be replaced with the values for the row being processed
#kafka_topic=maxwell
# alternative kafka topic to write DDL (alter/create/drop) to. Defaults to kafka_topic
#ddl_kafka_topic=maxwell_ddl
# hash function to use. "default" is just the JVM's 'hashCode' function.
#kafka_partition_hash=default # [default, murmur3]
# how maxwell writes its kafka key.
#
# 'hash' looks like:
# {"database":"test","table":"tickets","pk.id":10001}
#
# 'array' looks like:
# ["test","tickets",[{"id":10001}]]
#
# default: "hash"
#kafka_key_format=hash # [hash, array]
# extra kafka options. Anything prefixed "kafka." will get
# passed directly into the kafka-producer's config.
# a few defaults.
# These are 0.11-specific. They may or may not work with other versions.
#kafka.compression.type=snappy
#kafka.retries=0
#kafka.acks=1
#kafka.batch.size=16384
# kafka+SSL example
# kafka.security.protocol=SSL
# kafka.ssl.truststore.location=/var/private/ssl/kafka.client.truststore.jks
# kafka.ssl.truststore.password=test1234
# kafka.ssl.keystore.location=/var/private/ssl/kafka.client.keystore.jks
# kafka.ssl.keystore.password=test1234
# kafka.ssl.key.password=test1234#
# controls a heuristic check that maxwell may use to detect messages that
# we never heard back from. The heuristic check looks for "stuck" messages, and
# will timeout maxwell after this many milliseconds.
#
# See https://github.com/zendesk/maxwell/blob/master/src/main/java/com/zendesk/maxwell/producer/InflightMessageList.java
# if you really want to get into it.
#producer_ack_timeout=120000 # default 0
# *** partitioning ***
# What part of the data do we partition by?
#producer_partition_by=database # [database, table, primary_key, transaction_id, thread_id, column]
producer_partition_by=primary_key
# specify what fields to partition by when using producer_partition_by=column
# column separated list.
#producer_partition_columns=id,foo,bar
# when using producer_partition_by=column, partition by this when
# the specified column(s) don't exist.
producer_partition_by_fallback=database
# *** kinesis ***
kinesis_stream=social-dpl-prod
# AWS places a 256 unicode character limit on the max key length of a record
# http://docs.aws.amazon.com/kinesis/latest/APIReference/API_PutRecord.html
#
# Setting this option to true enables hashing the key with the md5 algorithm
# before we send it to kinesis so all the keys work within the key size limit.
# Values: true, false
# Default: false
#kinesis_md5_keys=true
# *** sqs ***
#sqs_queue_uri=aws_sqs_queue_uri
# The sqs producer will need aws credentials configured in the default
# root folder and file format. Please check below link on how to do it.
# http://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/setup-credentials.html
# *** pub/sub ***
#pubsub_project_id=maxwell
#pubsub_topic=maxwell
#ddl_pubsub_topic=maxwell_ddl
# *** rabbit-mq ***
#rabbitmq_host=rabbitmq_hostname
#rabbitmq_port=5672
#rabbitmq_user=guest
#rabbitmq_pass=guest
#rabbitmq_virtual_host=/
#rabbitmq_exchange=maxwell
#rabbitmq_exchange_type=fanout
#rabbitmq_exchange_durable=false
#rabbitmq_exchange_autodelete=false
#rabbitmq_routing_key_template=%db%.%table%
#rabbitmq_message_persistent=false
#rabbitmq_declare_exchange=true
# *** redis ***
#redis_host=redis_host
#redis_port=6379
#redis_auth=redis_auth
#redis_database=0
# name of pubsub/list/whatever key to publish to
#redis_key=maxwell
# this can be static, e.g. 'maxwell', or dynamic, e.g. namespace_%{database}_%{table}
#redis_pub_channel=maxwell
# this can be static, e.g. 'maxwell', or dynamic, e.g. namespace_%{database}_%{table}
#redis_list_key=maxwell
# this can be static, e.g. 'maxwell', or dynamic, e.g. namespace_%{database}_%{table}
# Valid values for redis_type = pubsub|lpush. Defaults to pubsub
#redis_type=pubsub
# *** custom producer ***
# the fully qualified class name for custom ProducerFactory
# see the following link for more details.
# http://maxwells-daemon.io/producers/#custom-producer
#custom_producer.factory=
# custom producer properties can be configured using the custom_producer.* property namespace
#custom_producer.custom_prop=foo
# *** filtering ***
# filter rows out of Maxwell's output. Command separated list of filter-rules, evaluated in sequence.
# A filter rule is:
# <type> ":" <db> "." <tbl> [ "." <col> "=" <col_val> ]
# type ::= [ "include" | "exclude" | "blacklist" ]
# db ::= [ "/regexp/" | "string" | "`string`" | "*" ]
# tbl ::= [ "/regexp/" | "string" | "`string`" | "*" ]
# col_val ::= "column_name"
# tbl ::= [ "/regexp/" | "string" | "`string`" | "*" ]
#
# See http://maxwells-daemon.io/filtering for more details
#
#filter= exclude: *.*, include: foo.*, include: bar.baz, include: foo.bar.col_eg = "value_to_match"
filter=exclude: delmondo.FacebookSubscriptionEvents,include: delmondo.*,exclude: delmondo.FacebookSubscriptionEvents
# javascript filter
# maxwell can run a bit of javascript for each row if you need very custom filtering/data munging.
# See http://maxwells-daemon.io/filtering/#javascript_filters for more details
#
#javascript=/path/to/javascript_filter_file
# *** encryption ***
# Encryption mode. Possible values are none, data, and all. (default none)
#encrypt=none
# Specify the secret key to be used
#secret_key=RandomInitVector
# *** monitoring ***
# Maxwell collects metrics via dropwizard. These can be exposed through the
# base logging mechanism (slf4j), JMX, HTTP or pushed to Datadog.
# Options: [jmx, slf4j, http, datadog]
# Supplying multiple is allowed.
#metrics_type=jmx,slf4j
# The prefix maxwell will apply to all metrics
#metrics_prefix=MaxwellMetrics # default MaxwellMetrics
# Enable (dropwizard) JVM metrics, default false
#metrics_jvm=true
# When metrics_type includes slf4j this is the frequency metrics are emitted to the log, in seconds
#metrics_slf4j_interval=60
# When metrics_type includes http or diagnostic is enabled, this is the port the server will bind to.
#http_port=8080
# When metrics_type includes http or diagnostic is enabled, this is the http path prefix, default /.
#http_path_prefix=/some/path/
# ** The following are Datadog specific. **
# When metrics_type includes datadog this is the way metrics will be reported.
# Options: [udp, http]
# Supplying multiple is not allowed.
#metrics_datadog_type=udp
# datadog tags that should be supplied
#metrics_datadog_tags=tag1:value1,tag2:value2
# The frequency metrics are pushed to datadog, in seconds
#metrics_datadog_interval=60
# required if metrics_datadog_type = http
#metrics_datadog_apikey=API_KEY
# required if metrics_datadog_type = udp
#metrics_datadog_host=localhost # default localhost
#metrics_datadog_port=8125 # default 8125
# Maxwell exposes http diagnostic endpoint to check below in parallel:
# 1. binlog replication lag
# 2. producer (currently kafka) lag
# To enable Maxwell diagnostic
#http_diagnostic=true # default false
# Diagnostic check timeout in milliseconds, required if diagnostic = true
#http_diagnostic_timeout=10000 # default 10000
# *** misc ***
# maxwell's bootstrapping functionality has a couple of modes.
#
# In "async" mode, maxwell will output the replication stream while it
# simultaneously outputs the database to the topic. Note that it won't
# output replication data for any tables it is currently bootstrapping -- this
# data will be buffered and output after the bootstrap is complete.
#
# In "sync" mode, maxwell stops the replication stream while it
# outputs bootstrap data.
#
# async mode keeps ops live while bootstrapping, but carries the possibility of
# data loss (due to buffering transactions). sync mode is safer but you
# have to stop replication.
#bootstrapper=async [sync, async, none]
# output filename when using the "file" producer
#output_file=/path/to/file
kinesis-producer-library.properties
# copied from https://github.com/awslabs/amazon-kinesis-producer/blob/master/java/amazon-kinesis-producer-sample/default_config.properties
# You can load a properties file with
# KinesisProducerConfiguration.fromPropertiesFile(String)
#
# Any fields not found in the properties file will take on default values.
#
# The values loaded are checked against any constraints that each respective
# field may have. If there are invalid values an IllegalArgumentException will
# be thrown.
# Enable aggregation. With aggregation, multiple user records are packed into
# a single KinesisRecord. If disabled, each user record is sent in its own
# KinesisRecord.
#
# If your records are small, enabling aggregation will allow you to put many
# more records than you would otherwise be able to for a shard before getting
# throttled.
#
# Default: true
AggregationEnabled = true
# Maximum number of items to pack into an aggregated record.
#
# There should be normally no need to adjust this. If you want to limit the
# time records spend buffering, look into record_max_buffered_time instead.
#
# Default: 4294967295
# Minimum: 1
# Maximum (inclusive): 9223372036854775807
AggregationMaxCount = 4294967295
# Maximum number of bytes to pack into an aggregated Kinesis record.
#
# There should be normally no need to adjust this. If you want to limit the
# time records spend buffering, look into record_max_buffered_time instead.
#
# If a record has more data by itself than this limit, it will bypass the
# aggregator. Note the backend enforces a limit of 50KB on record size. If
# you set this beyond 50KB, oversize records will be rejected at the backend.
#
# Default: 51200
# Minimum: 64
# Maximum (inclusive): 1048576
AggregationMaxSize = 51200
# Maximum number of items to pack into an PutRecords request.
#
# There should be normally no need to adjust this. If you want to limit the
# time records spend buffering, look into record_max_buffered_time instead.
#
# Default: 500
# Minimum: 1
# Maximum (inclusive): 500
CollectionMaxCount = 500
# Maximum amount of data to send with a PutRecords request.
#
# There should be normally no need to adjust this. If you want to limit the
# time records spend buffering, look into record_max_buffered_time instead.
#
# Records larger than the limit will still be sent, but will not be grouped
# with others.
#
# Default: 5242880
# Minimum: 52224
# Maximum (inclusive): 9223372036854775807
CollectionMaxSize = 5242880
# Timeout (milliseconds) for establishing TLS connections.
#
# Default: 6000
# Minimum: 100
# Maximum (inclusive): 300000
ConnectTimeout = 6000
# Use a custom Kinesis and CloudWatch endpoint.
#
# Mostly for testing use. Note this does not accept protocols or paths, only
# host names or ip addresses. There is no way to disable TLS. The KPL always
# connects with TLS.
#
# Expected pattern: ^([A-Za-z0-9-\.]+)?$
# CustomEndpoint =
# If true, throttled puts are not retried. The records that got throttled
# will be failed immediately upon receiving the throttling error. This is
# useful if you want to react immediately to any throttling without waiting
# for the KPL to retry. For example, you can use a different hash key to send
# the throttled record to a backup shard.
#
# If false, the KPL will automatically retry throttled puts. The KPL performs
# backoff for shards that it has received throttling errors from, and will
# avoid flooding them with retries. Note that records may fail from
# expiration (see record_ttl) if they get delayed for too long because of
# throttling.
#
# Default: false
FailIfThrottled = false
# Minimum level of logs. Messages below the specified level will not be
# logged. Logs for the native KPL daemon show up on stderr.
#
# Default: info
# Expected pattern: info|warning|error
LogLevel = info
# Maximum number of connections to open to the backend. HTTP requests are
# sent in parallel over multiple connections.
#
# Setting this too high may impact latency and consume additional resources
# without increasing throughput.
#
# Default: 24
# Minimum: 1
# Maximum (inclusive): 256
MaxConnections = 24
# Controls the granularity of metrics that are uploaded to CloudWatch.
# Greater granularity produces more metrics.
#
# When "shard" is selected, metrics are emitted with the stream name and
# shard id as dimensions. On top of this, the same metric is also emitted
# with only the stream name dimension, and lastly, without the stream name.
# This means for a particular metric, 2 streams with 2 shards (each) will
# produce 7 CloudWatch metrics, one for each shard, one for each stream, and
# one overall, all describing the same statistics, but at different levels of
# granularity.
#
# When "stream" is selected, per shard metrics are not uploaded; when
# "global" is selected, only the total aggregate for all streams and all
# shards are uploaded.
#
# Consider reducing the granularity if you're not interested in shard-level
# metrics, or if you have a large number of shards.
#
# If you only have 1 stream, select "global"; the global data will be
# equivalent to that for the stream.
#
# Refer to the metrics documentation for details about each metric.
#
# Default: shard
# Expected pattern: global|stream|shard
MetricsGranularity = shard
# Controls the number of metrics that are uploaded to CloudWatch.
#
# "none" disables all metrics.
#
# "summary" enables the following metrics: UserRecordsPut, KinesisRecordsPut,
# ErrorsByCode, AllErrors, BufferingTime.
# "detailed" enables all remaining metrics.
#
# Refer to the metrics documentation for details about each metric.
#
# Default: detailed
# Expected pattern: none|summary|detailed
MetricsLevel = detailed
# The namespace to upload metrics under.
#
# If you have multiple applications running the KPL under the same AWS
# account, you should use a different namespace for each application.
#
# If you are also using the KCL, you may wish to use the application name you
# have configured for the KCL as the the namespace here. This way both your
# KPL and KCL metrics show up under the same namespace.
#
# Default: KinesisProducerLibrary
# Expected pattern: (?!AWS/).{1,255}
MetricsNamespace = KinesisProducerLibrary
# Delay (in milliseconds) between each metrics upload.
#
# For testing only. There is no benefit in setting this lower or higher in
# production.
#
# Default: 60000
# Minimum: 1
# Maximum (inclusive): 60000
MetricsUploadDelay = 60000
# Minimum number of connections to keep open to the backend.
#
# There should be no need to increase this in general.
#
# Default: 1
# Minimum: 1
# Maximum (inclusive): 16
MinConnections = 1
# Server port to connect to. Only useful with custom_endpoint.
#
# Default: 443
# Minimum: 1
# Maximum (inclusive): 65535
Port = 443
# Limits the maximum allowed put rate for a shard, as a percentage of the
# backend limits.
#
# The rate limit prevents the producer from sending data too fast to a shard.
# Such a limit is useful for reducing bandwidth and CPU cycle wastage from
# sending requests that we know are going to fail from throttling.
#
# Kinesis enforces limits on both the number of records and number of bytes
# per second. This setting applies to both.
#
# The default value of 150% is chosen to allow a single producer instance to
# completely saturate the allowance for a shard. This is an aggressive
# setting. If you prefer to reduce throttling errors rather than completely
# saturate the shard, consider reducing this setting.
#
# Default: 150
# Minimum: 1
# Maximum (inclusive): 9223372036854775807
RateLimit = 150
# Maximum amount of itme (milliseconds) a record may spend being buffered
# before it gets sent. Records may be sent sooner than this depending on the
# other buffering limits.
#
# This setting provides coarse ordering among records - any two records will
# be reordered by no more than twice this amount (assuming no failures and
# retries and equal network latency).
#
# The library makes a best effort to enforce this time, but cannot guarantee
# that it will be precisely met. In general, if the CPU is not overloaded,
# the library will meet this deadline to within 10ms.
#
# Failures and retries can additionally increase the amount of time records
# spend in the KPL. If your application cannot tolerate late records, use the
# record_ttl setting to drop records that do not get transmitted in time.
#
# Setting this too low can negatively impact throughput.
#
# Default: 100
# Maximum (inclusive): 9223372036854775807
RecordMaxBufferedTime = 100
# Set a time-to-live on records (milliseconds). Records that do not get
# successfully put within the limit are failed.
#
# This setting is useful if your application cannot or does not wish to
# tolerate late records. Records will still incur network latency after they
# leave the KPL, so take that into consideration when choosing a value for
# this setting.
#
# If you do not wish to lose records and prefer to retry indefinitely, set
# record_ttl to a large value like INT_MAX. This has the potential to cause
# head-of-line blocking if network issues or throttling occur. You can
# respond to such situations by using the metrics reporting functions of the
# KPL. You may also set fail_if_throttled to true to prevent automatic
# retries in case of throttling.
#
# Default: 30000
# Minimum: 100
# Maximum (inclusive): 9223372036854775807
RecordTtl = 60000
# Which region to send records to.
#
# If you do not specify the region and are running in EC2, the library will
# use the region the instance is in.
#
# The region is also used to sign requests.
#
# Expected pattern: ^([a-z]+-[a-z]+-[0-9])?$
Region = us-east-1
# The maximum total time (milliseconds) elapsed between when we begin a HTTP
# request and receiving all of the response. If it goes over, the request
# will be timed-out.
#
# Note that a timed-out request may actually succeed at the backend. Retrying
# then leads to duplicates. Setting the timeout too low will therefore
# increase the probability of duplicates.
#
# Default: 6000
# Minimum: 100
# Maximum (inclusive): 600000
RequestTimeout = 6000
# Verify the endpoint's certificate. Do not disable unless using
# custom_endpoint for testing. Never disable this in production.
#
# Default: true
VerifyCertificate = true
然后还需要配合拥有 aws 相关生产者组件的 iam 权限
"kinesis:PutRecord" "kinesis:PutRecords" "kinesis:DescribeStream"
当这些配置完毕之后可以直接去 maxwell 的根目录下执行
bin/maxwell
即可，这个命令会先去读 config.properties 配置，然后发现是使用 kinesis 会自己去读另外一个 kinesis 相关的配置文件。
Consumer:
我是使用 python 的 KCL 客户端，但是这个客户端并不是一个纯粹 python 实现的客户端，最终还是需要使用 java 来启的，所以我们需要 jdk7 以上的 jdk 被安装在部署的机器上。
https://docs.aws.amazon.com/streams/latest/dev/kcl2-standard-consumer-python-example.html
提供了一个例子，其中包括了一个 pipeline 的处理，以及一套配置文件的默认值，我们可以在本地初始化这两个文件。
另外我们可以在 https://github.com/awslabs/amazon-kinesis-client-python 找到启动消费者的整个流程。
使用
`amazon_kclpy_helper.py --print_command
--java <path-to-java> --properties samples/sample.properties`
来启动我们的 python 消费者。同样的认证方式，我们需要将具有 aws 相关权限的 access_key access_secret 放到 ~/.aws/credentials 文件中用以鉴权。
Reference:
https://maxwells-daemon.io/producers/#kinesis
https://docs.aws.amazon.com/streams/latest/dev/amazon-kinesis-streams.html
http://maxwells-daemon.io/quickstart
https://github.com/awslabs/amazon-kinesis-client-python
https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/setup-credentials.html
https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/credentials.html