#kafkatips search results

When you see some serious skew like this, it's time to reassign partitions & reelect leaders. #kafkatips

getconduktor's tweet image. When you see some serious skew like this, it's time to reassign partitions & reelect leaders. #kafkatips

It can be interesting to look at topic partitions from the broker side. You could realize that your topic partition leaders are not properly spread among all of them. #kafkatips

getconduktor's tweet image. It can be interesting to look at topic partitions from the broker side. You could realize that your topic partition leaders are not properly spread among all of them. #kafkatips

In Kafka, partitioning by hash of a key can lead to uneven data distribution if there's a skewed key distribution, causing some partitions to become hotspots. Use consistent hashing or range-based partitioning instead for more even data distribution. #KafkaTips #DataDistribution


Dato curioso: Sabía usted que pedir "me gusta" en #instagram lo hace ver un 50.000% más ridículo que si lo hace en Facebook? #KafkaTips


When consuming Kafka messages, set `max.partition.fetch.bytes` to a reasonable value (e.g., 1MB) to avoid overwhelming your application with too many messages at once, improving performance and reducing memory usage. #KafkaTips #ApacheKafka


In Kafka, partitioning is not only about distributing data but also determines the order of message processing. Consider using `in_order=True` when setting up partitions to ensure messages within each partition are processed sequentially. #KafkaTips


In Kafka, partitioning keys can affect data ordering & consistency. When using a single partitioned topic, messages with the same key will always be processed in order, but if multiple partitions are used, message order is no longer guaranteed #KafkaTips


In Kafka, partitioning is typically done based on a key (e.g., user ID). However, if your keys have a large range of values, consider using a custom partitioner to ensure even distribution & avoid hotspots by mapping values to specific partitions using a hash function. #KafkaTips


When working with Kafka, remember that partitioning key should be designed to ensure even data distribution across partitions, as uneven distribution can lead to performance issues. Use a combination of timestamp and user_id as the partitioning key to achieve this. #KafkaTips ...


In Kafka, partitioning by hash of a key can lead to uneven data distribution if there's a skewed key distribution, causing some partitions to become hotspots. Use consistent hashing or range-based partitioning instead for more even data distribution. #KafkaTips #DataDistribution


When consuming Kafka messages, set `max.partition.fetch.bytes` to a reasonable value (e.g., 1MB) to avoid overwhelming your application with too many messages at once, improving performance and reducing memory usage. #KafkaTips #ApacheKafka


When working with Kafka, remember that partitioning key should be designed to ensure even data distribution across partitions, as uneven distribution can lead to performance issues. Use a combination of timestamp and user_id as the partitioning key to achieve this. #KafkaTips ...


In Kafka, partitioning is typically done based on a key (e.g., user ID). However, if your keys have a large range of values, consider using a custom partitioner to ensure even distribution & avoid hotspots by mapping values to specific partitions using a hash function. #KafkaTips


In Kafka, partitioning keys can affect data ordering & consistency. When using a single partitioned topic, messages with the same key will always be processed in order, but if multiple partitions are used, message order is no longer guaranteed #KafkaTips


In Kafka, partitioning is not only about distributing data but also determines the order of message processing. Consider using `in_order=True` when setting up partitions to ensure messages within each partition are processed sequentially. #KafkaTips


When you see some serious skew like this, it's time to reassign partitions & reelect leaders. #kafkatips

getconduktor's tweet image. When you see some serious skew like this, it's time to reassign partitions & reelect leaders. #kafkatips

It can be interesting to look at topic partitions from the broker side. You could realize that your topic partition leaders are not properly spread among all of them. #kafkatips

getconduktor's tweet image. It can be interesting to look at topic partitions from the broker side. You could realize that your topic partition leaders are not properly spread among all of them. #kafkatips

Dato curioso: Sabía usted que pedir "me gusta" en #instagram lo hace ver un 50.000% más ridículo que si lo hace en Facebook? #KafkaTips


When you see some serious skew like this, it's time to reassign partitions & reelect leaders. #kafkatips

getconduktor's tweet image. When you see some serious skew like this, it's time to reassign partitions & reelect leaders. #kafkatips

It can be interesting to look at topic partitions from the broker side. You could realize that your topic partition leaders are not properly spread among all of them. #kafkatips

getconduktor's tweet image. It can be interesting to look at topic partitions from the broker side. You could realize that your topic partition leaders are not properly spread among all of them. #kafkatips

Loading...

Something went wrong.


Something went wrong.


United States Trends