A method performed by one or more data processing apparatus comprising: receiving, as input at one or more data processing apparatus, an original model and a new model that was generated from the original model but differs from the original model, wherein each of the original model and the new model include populations assigned to nodes of the models; wherein each of the original model and the new model are configured to receive an input of an identifier indicating data and output a particular location in memory to which the data is assigned to be allocated; mapping, by the one or more data processing apparatus, structures of the new model to structures of the original model; classifying, by the one or more data processing apparatus and based on the mapping, each structure of the new model as belonging to a group of structures sharing at least one characteristic; naming, by the one or more data processing apparatus and based on the classifying, the groups of structures of the new model to match names of groups of structures of the original model; generating, by the one or more data processing apparatus and based on the mapping and the classifying, a merged model by: setting up population pools of the merged model by reusing a population for any node common to both the original model and the new model, wherein nodes common to both the original model and the new model are identified based on the naming, and performing a node by node adjustment to minimize differences between the merged model and the original model while retaining the aggregated statistical properties of the new model; and classifying, by the one or more data processing apparatus and using the merged model, a plurality of unique entities by applying consistent hashing to each of the plurality of unique entities, including: assigning, using the merged model, persistent identifiers to each of a plurality of unique entities, wherein the persistent identifier classifies the unique entity into a particular group of structures in the new model. receiving, as input at one or more data processing apparatus, an original model and a new model that was generated from the original model but differs from the original model, wherein each of the original model and the new model include populations assigned to nodes of the models; wherein each of the original model and the new model are configured to receive an input of an identifier indicating data and output a particular location in memory to which the data is assigned to be allocated; mapping, by the one or more data processing apparatus, structures of the new model to structures of the original model; classifying, by the one or more data processing apparatus and based on the mapping, each structure of the new model as belonging to a group of structures sharing at least one characteristic; naming, by the one or more data processing apparatus and based on the classifying, the groups of structures of the new model to match names of groups of structures of the original model; generating, by the one or more data processing apparatus and based on the mapping and the classifying, a merged model by: setting up population pools of the merged model by reusing a population for any node common to both the original model and the new model, wherein nodes common to both the original model and the new model are identified based on the naming, and performing a node by node adjustment to minimize differences between the merged model and the original model while retaining the aggregated statistical properties of the new model; and setting up population pools of the merged model by reusing a population for any node common to both the original model and the new model, wherein nodes common to both the original model and the new model are identified based on the naming, and performing a node by node adjustment to minimize differences between the merged model and the original model while retaining the aggregated statistical properties of the new model; and classifying, by the one or more data processing apparatus and using the merged model, a plurality of unique entities by applying consistent hashing to each of the plurality of unique entities, including: assigning, using the merged model, persistent identifiers to each of a plurality of unique entities, wherein the persistent identifier classifies the unique entity into a particular group of structures in the new model.