A Good hack Anthony and Vito. Short and Simple.
On Fri, Aug 14, 2015 at 1:50 AM, Anthony Baker <aba...@pivotal.io> wrote: > Thanks for the suggestions Mike. At this point we are just exploring > ideas and putting them out for discussion. Regarding restricting access to > this feature, we used the Geode Java client so standard security and > authorizations would apply. > > Anthony > > > On Aug 13, 2015, at 12:24 PM, Michael Stolz <mst...@pivotal.io> wrote: > > > > If this feature makes it into an actual release please make sure this > > option is not enabled by default and is securely turned off for > > environments where there are strong controls around releasing software > into > > production. > > Also make sure that it is secured in terms of Authentication and > > Authorization via the Geode security framework when it is enabled, so > that > > not just anyone can push code. > > > > -- > > Mike Stolz > > Principal Technical Account Manager > > Mobile: 631-835-4771 > > > > On Thu, Aug 13, 2015 at 1:57 PM, Anthony Baker <aba...@pivotal.io > <mailto:aba...@pivotal.io>> wrote: > > > >> Vito and I spent some time hacking up a prototype for dynamic and > >> distributed classloading of Geode functions. Currently a user has to > >> compile a function into a jar and deploy it using gfsh before it can be > >> executed. If we could enable automatic deployment of functions across a > >> running cluster it would speed up the development cycle for Geode > >> applications and pave the way for other interesting features (like Java8 > >> lambdas). > >> > >> Here’s how it works: > >> > >> A function wrapper (DynamicFunction) serializes the original function > >> object and captures dependent classes as byte arrays. We generate an > MD5 > >> hash over the bytecode and use that as the key for storing the bytecode > in > >> a replicated region (“hackday”) within the cache. When the function is > >> invoked, we call putIfAbsent() to distribute the byte code prior to > >> executing the function across the cluster. During execution, we extend > the > >> TCCL with a new class loader that loads classes from our region while > the > >> original function is being deserialized. The original function is then > >> executed in parallel on the cluster members. This allows an application > >> developer to iteratively modify and test function code without any > manual > >> steps to upload class files. > >> > >> Obviously, there is a lot more thinking and design work to do around > these > >> ideas. Here’s our super-hacky code if you’re interested: > >> https://gist.github.com/metatype/9b1f39a24e52f5c6f3e1 < > >> https://gist.github.com/metatype/9b1f39a24e52f5c6f3e1 < > https://gist.github.com/metatype/9b1f39a24e52f5c6f3e1>> > >> > >> Caveats: > >> > >> 1) Currently we only capture static class dependencies. Any class > >> dependencies present during method invocations are ignored. This could > be > >> addressed by doing byte code inspection (using ASM, javaassist, etc). > >> > >> 2) The region we use to cache class byte code should be automatically > >> recreated as a metadata region, similar to how we store pdx types. We > also > >> need to configure eviction and expiration attributes to control resource > >> usage and remove garbage. > >> > >> 3) We only injected the byte code caching hack into the code path for > >> FunctionService.onServers(pool). Also, the putIfAbsent() call adds > another > >> network roundtrip. > >> > >> > >> Anthony & Vito > >
