big-data
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Problem: the approximate method can still be slow for many trees
catboost version: master
Operating System: ubuntu 18.04
CPU: i9
GPU: RTX2080
Would be good to be able to specify how many trees to use for shapley. The model.predict and prediction_type versions allow this. lgbm/xgb allow this.
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Please describe the problem you are trying to solve
I would like to evict entries based on their creation time. I want to evict the oldest ones first.
Please describe the desired behavior
Basically FIFO eviction. I would like to specify directly in the configuration something like:
<eviction eviction-policy="FIFO" max-size-policy="PER_NODE" size="5000"/>
**Describe alte
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PrestoDB https://prestodb.io .. is widely used as SQL frontend for many different data-sources, including ElasticSearch, and even files in S3 .. would be very nice if there would be a Connector available for Vespa.
Hi, if my spark app is using 2 storage type, both S3 and Azure Data Lake Store Gen2, could I put spark.delta.logStore.class=org.apache.spark.sql.delta.storage.AzureLogStore, org.apache.spark.sql.delta.storage.S3SingleDriverLogStore
Thanks in advance
We should remove Guava as a dependency from the server module.
Most of the functionality we used from Guava are provided by the JDK by now.
The stuff that is missing from the JDK has mostly been added in the utilities we inherited from Elasticsearch, due to their decision to remove Guava completely.
Any functionality that we use which isn't present in the JDK can be co
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Now insert and query share the resource ( Max Process Count control) 。 When the query with high TPS,the insert will get error (“error: too many process”). I think separator the resource for Insert and Query will makes sense. Ensure enough resource for insert。It looks like Use Yarn, Insert and Query use the different resource quota。
Or the simple way , Can we set Ratio for Insert and