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https://issues.apache.org/jira/browse/IGNITE-18209?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ilya Shishkov updated IGNITE-18209:
-----------------------------------
Description:
Currently, there is a bottleneck in synchronized method
{{KafkaToIgniteMetadataUpdater#updateMetadata}}:
# {{KafkaToIgniteCdcStreamer}} contains multiple
{{KafkaToIgniteCdcStreamerApplier}} which shares _single_
{{KafkaToIgniteMetadataUpdater}}.
# All appliers handle corrsponding partitions consequently.
# {{META_UPDATE_MARKER}} is sent twice to each Kafka partition of event topic:
firstly, in case of type mappings updates, secondly, in case of binary types
update.
# When first {{KafkaToIgniteCdcStreamerApplier}} meets {{META_UPDATE_MARKER}}
it calls {{KafkaToIgniteMetadataUpdater#updateMetadata}} which in turn calls
{{KafkaConsumer#poll}}, which returns immediately [1] when data is present in
metadata topic. If there are few binary types and mappings to update, some
{{KafkaToIgniteCdcStreamerApplier}} thread will consume all entries from
metadata topic.
# All other threads of all {{KafkaToIgniteCdcStreamerApplier}} will call
{{KafkaConsumer#poll}} from empty metadata topic, which will remain blocked
until new data becomes available or request timeout occurs [1].
# Because of {{synchronized}} access to
{{KafkaToIgniteMetadataUpdater#updateMetadata}} all threads of all
{{KafkaToIgniteCdcStreamerApplier}} will form a sequence of calls. Each call
will block remaining appliers threads for {{kafkaReqTimeout}} period (if
metadata topic remains empty).
# The last call, i.e. last Kafka partition polling in this chain will happen at
least after {{(partitionsCount x 2 - 1) x kafkaReqTimeout}} period. For example
for default timeout and 16 Kafka partitions _last partition will be consumed
after 1.5 minutes_.
# Amount of threads in {{KafkaToIgniteCdcStreamer}} does not make sence.
# Data updates are blocked for Kafka partitions with unhandled update markers.
# As I understand possible solutions are
# Hold information about replicated types or get it from {{BinaryContext}}.
Information about type can be sent with {{META_UPDATE_MARKER}}.
# Completely remove metadata topic, and send metadata merged with marker
directly into event topic.
# Any other ways to sync appliers?
As a PoC of approach with {{BinaryContext}} I have prepared PR [2].
Links:
#
https://kafka.apache.org/27/javadoc/org/apache/kafka/clients/consumer/KafkaConsumer.html#poll-java.time.Duration-
# https://github.com/apache/ignite-extensions/pull/187
was:
Currently, there is a bottleneck in synchronized method
{{KafkaToIgniteMetadataUpdater#updateMetadata}}:
# {{KafkaToIgniteCdcStreamer}} contains multiple
{{KafkaToIgniteCdcStreamerApplier}} which shares _single_
{{KafkaToIgniteMetadataUpdater}}.
# All appliers handle corrsponding partitions consequently.
# {{META_UPDATE_MARKER}} is sent twice to each Kafka partition of event topic:
firstly, in case of type mappings updates, secondly, in case of binary types
update.
# When first {{KafkaToIgniteCdcStreamerApplier}} meets {{META_UPDATE_MARKER}}
it calls {{KafkaToIgniteMetadataUpdater#updateMetadata}} which in turn calls
{{KafkaConsumer#poll}}, which returns immediately [1] when data is present in
metadata topic. If there are few binary types and mappings to update, some
{{KafkaToIgniteCdcStreamerApplier}} thread will consume all entries from
metadata topic.
# All other threads of all {{KafkaToIgniteCdcStreamerApplier}} will call
{{KafkaConsumer#poll}} from empty metadata topic, which will remain blocked
until new data becomes available or request timeout occurs [1].
# Because of {{synchronized}} access to
{{KafkaToIgniteMetadataUpdater#updateMetadata}} all threads of all
{{KafkaToIgniteCdcStreamerApplier}} will form a sequence of calls. Each call
will block remaining appliers threads for {{kafkaReqTimeout}} period (if
metadata topic remains empty).
# The last call, i.e. last Kafka partition polling in this chain will happen at
least after {{(partitionsCount x 2 - 1) x kafkaReqTimeout}} period. For example
for default timeout and 16 Kafka partitions _last partition will be consumed
after 1.5 minutes_.
# Amount of threads in {{KafkaToIgniteCdcStreamer}} does not make sence.
# Data updates are blocked for Kafka partitions with unhandled update markers.
As I understand possible solutions are
* Hold information about replicated types or get it from {{BinaryContext}}.
Information about type can be sent with {{META_UPDATE_MARKER}}.
* Any other ways to sync appliers?
As a PoC of approach with {{BinaryContext}} I have prepared PR [2].
Links:
#
https://kafka.apache.org/27/javadoc/org/apache/kafka/clients/consumer/KafkaConsumer.html#poll-java.time.Duration-
# https://github.com/apache/ignite-extensions/pull/187
> Reduce binary metadata synchronization time for CDC through Kafka
> -----------------------------------------------------------------
>
> Key: IGNITE-18209
> URL: https://issues.apache.org/jira/browse/IGNITE-18209
> Project: Ignite
> Issue Type: Improvement
> Components: extensions
> Reporter: Ilya Shishkov
> Assignee: Ilya Shishkov
> Priority: Major
> Labels: IEP-59, ise
>
> Currently, there is a bottleneck in synchronized method
> {{KafkaToIgniteMetadataUpdater#updateMetadata}}:
> # {{KafkaToIgniteCdcStreamer}} contains multiple
> {{KafkaToIgniteCdcStreamerApplier}} which shares _single_
> {{KafkaToIgniteMetadataUpdater}}.
> # All appliers handle corrsponding partitions consequently.
> # {{META_UPDATE_MARKER}} is sent twice to each Kafka partition of event
> topic: firstly, in case of type mappings updates, secondly, in case of binary
> types update.
> # When first {{KafkaToIgniteCdcStreamerApplier}} meets {{META_UPDATE_MARKER}}
> it calls {{KafkaToIgniteMetadataUpdater#updateMetadata}} which in turn calls
> {{KafkaConsumer#poll}}, which returns immediately [1] when data is present in
> metadata topic. If there are few binary types and mappings to update, some
> {{KafkaToIgniteCdcStreamerApplier}} thread will consume all entries from
> metadata topic.
> # All other threads of all {{KafkaToIgniteCdcStreamerApplier}} will call
> {{KafkaConsumer#poll}} from empty metadata topic, which will remain blocked
> until new data becomes available or request timeout occurs [1].
> # Because of {{synchronized}} access to
> {{KafkaToIgniteMetadataUpdater#updateMetadata}} all threads of all
> {{KafkaToIgniteCdcStreamerApplier}} will form a sequence of calls. Each call
> will block remaining appliers threads for {{kafkaReqTimeout}} period (if
> metadata topic remains empty).
> # The last call, i.e. last Kafka partition polling in this chain will happen
> at least after {{(partitionsCount x 2 - 1) x kafkaReqTimeout}} period. For
> example for default timeout and 16 Kafka partitions _last partition will be
> consumed after 1.5 minutes_.
> # Amount of threads in {{KafkaToIgniteCdcStreamer}} does not make sence.
> # Data updates are blocked for Kafka partitions with unhandled update markers.
> # As I understand possible solutions are
> # Hold information about replicated types or get it from {{BinaryContext}}.
> Information about type can be sent with {{META_UPDATE_MARKER}}.
> # Completely remove metadata topic, and send metadata merged with marker
> directly into event topic.
> # Any other ways to sync appliers?
> As a PoC of approach with {{BinaryContext}} I have prepared PR [2].
> Links:
> #
> https://kafka.apache.org/27/javadoc/org/apache/kafka/clients/consumer/KafkaConsumer.html#poll-java.time.Duration-
> # https://github.com/apache/ignite-extensions/pull/187
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