zhztheplayer commented on a change in pull request #7030:
URL: https://github.com/apache/arrow/pull/7030#discussion_r440075959



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File path: 
java/dataset/src/main/java/org/apache/arrow/memory/NativeUnderlingMemory.java
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@@ -0,0 +1,65 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.arrow.memory;
+
+import org.apache.arrow.dataset.jni.JniWrapper;
+
+/**
+ * AllocationManager implementation for Native allocated memory.
+ */
+public class NativeUnderlingMemory extends AllocationManager {

Review comment:
       Hi @jacques-n,
   
   Thanks for your suggestion but I think the "weaknesses" of current 
implementation might be somehow overrated. Specifically for orc adaptor it's of 
course urgent to improve its memory allocation process because all of the 
allocated memory by orc is escaping from Java allocator. However we don't have 
the same problem here in Datasets as allocator is going to manage all the 
native memory. The only issue we may have is that OOM error might be thrown 
after a buffer is allocated rather than before we create it. But because OOM 
will be finally thrown and over-allocated memory will be finally released, the 
impact will be very very small.
   
   And I knew we can possibly change to allocate Java memory directly from C++ 
via JNI, but codes will become messy and performance will turn to worse. That 
is why I didn't propose such a solution in this PR.




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