From accb361ba60b674a0df3e15cefa752ae7fe012f5 Mon Sep 17 00:00:00 2001 From: Rob Austin Date: Mon, 10 Nov 2014 18:01:34 +0000 Subject: [PATCH] updated readme --- README.md | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 98484c987..c2bd8d6af 100644 --- a/README.md +++ b/README.md @@ -1454,8 +1454,8 @@ an interface as the data type, the map will generate the implementation. ### Improving the performance of Chronicle Maps Serialization -Are you finding that when using Chronicle Map the serialisation is slow, but you have a large object graph - +Are you finding that when using Chronicle Map the serialisation is slow, but you have a large object graph, +if so - here are some steps that you can take to improve performance ( you may find that just one of these steps gives you the performance you require ) * consider replacing java.io.Serializable with java.io.Externalizable ( java.io.Externalizable can be much @@ -1463,7 +1463,11 @@ faster, but requires you do the the serialisation your self) * consider using net.openhft.lang.io.serialization.BytesMarshallable, working with this is like using java .io.Externalizable but in addition is supports some compressed types, which can give you slightly better performance. * try where possible to de-normalize your object graph, if you are able to limit your graph down to just a -few types you can then look at using our off heap interface proxy object and have a map instance per type of object ( much like in a database where you would have a table per type ), could could then set up foreign key relationships between the maps. +few types you can then look at using our off heap interface proxy objects and have a map instance per type +of object + ( much like in a database where you would have a table per type ), you could then set up foreign key + relationships between the maps, by using for example integers or longs. This works especially well as + integers and longs have a good hash distribution. ### Tuning Chronicle Map with Large Data