It is important that all nodes that process a transaction always agree on whether it is valid or not. Because transaction types are defined using JVM byte code, this means that the execution of that byte code must be fully deterministic. Out of the box a standard JVM is not fully deterministic, thus we must make some modifications in order to satisfy our requirements.
So, what does it mean for a piece of code to be fully deterministic? Ultimately, it means that the code, when viewed as a function, is pure. In other words, given the same set of inputs, it will always produce the same set of outputs without inflicting any side-effects that might later affect the computation.
The code in the DJVM module has not yet been integrated with the rest of the platform. It will eventually become a part of the node and enforce deterministic and secure execution of smart contract code, which is mobile and may propagate around the network without human intervention.
Currently, it stands alone as an evaluation version. We want to give developers the ability to start trying it out and get used to developing deterministic code under the set of constraints that we envision will be placed on contract code in the future.
For a program running on the JVM, non-determinism could be introduced by a range of sources, for instance:
- External input, e.g., the file system, network, system properties and clocks.
- Random number generators.
- Halting criteria, e.g., different decisions about when to terminate long running programs.
- Hash-codes, or more specifically
Object.hashCode(), which is typically implemented either by returning a pointer address or by assigning the object a random number. This could, for instance, surface as different iteration orders over hash maps and hash sets, or be used as non-pure input into arbitrary expressions.
- Differences in hardware floating point arithmetic.
- Multi-threading and consequent differences in scheduling strategies, affinity, etc.
- Differences in API implementations between nodes.
- Garbage collector callbacks.
To ensure that the contract verification function is fully pure even in the face of infinite loops we want to use a custom-built JVM sandbox. The sandbox performs static analysis of loaded byte code and a rewriting pass to allow for necessary instrumentation and constraint hardening.
The byte code rewriting further allows us to patch up and control the default behaviour of things like the hash-code
java.lang.Object. Contract code is rewritten the first time it needs to be executed and then stored
for future use.
The sandbox is abstracted away as an executor which takes as input an implementation of the interface
Function<in Input, out Output>, dereferenced by a
ClassSource. This interface has a single method that
needs implementing, namely
ClassSource object referencing such an implementation can be passed into the
SandboxExecutor<in Input, out Output> together with an input of type
Input. The executor has operations for both execution and static
validate(). These methods both return a summary object.
- Whether or not the runnable was successfully executed.
- If successful, the return value of
- If failed, the exception that was raised.
- And in both cases, a summary of all accrued costs during execution.
- A type hierarchy of classes and interfaces loaded and touched by the sandbox’s class loader during analysis, each of which contain information about the respective transformations applied as well as meta-data about the types themselves and all references made from said classes.
- A list of messages generated during the analysis. These can be of different severity, and only messages of
ERRORwill prevent execution.
The sandbox has a configuration that applies to the execution of a specific runnable. This configuration, on a higher level, contains a set of rules, definition providers and emitters.
The set of rules is what defines the constraints posed on the runtime environment. A rule can act on three different
levels, namely on a type-, member- or instruction-level. The set of rules get processed and validated by the
RuleValidator prior to execution.
Similarly, there is a set of definition providers which can be used to modify the definition of either a type or a type’s members. This is what controls things like ensuring that all methods implement strict floating point arithmetic, and normalisation of synchronised methods.
Lastly, there is a set of emitters. These are used to instrument the byte code for cost accounting purposes, and also
to inject code for checks that we want to perform at runtime or modifications to out-of-the-box behaviour. Many of
these emitters will rewrite non-deterministic operations to throw
RuleViolationError exceptions instead, which
means that the ultimate proof that a function is truly deterministic is that it executes successfully inside the DJVM.
Static Byte Code Analysis
In summary, the byte code analysis currently performs the following checks. This is not an exhaustive list as further work may well introduce additional constraints that we would want to place on the sandbox environment.
Disallow Catching ThreadDeath Exception
Prevents exception handlers from catching
ThreadDeath exceptions. If the developer attempts to catch an
Throwable (both being transitive parent types of
ThreadDeath), an explicit check will be injected into the
byte code to verify that exceptions that are trying to kill the current thread are not being silenced. Consequently,
the user will not be able to bypass an exit signal.
Disallow Catching ThresholdViolationException
ThresholdViolationException is, as the name suggests, used to signal to the sandbox that a cost tracked by the
runtime cost accountant has been breached. For obvious reasons, the sandbox needs to protect against user code that
tries to catch such exceptions, as doing so would allow the user to bypass the thresholds set out in the execution
Disallow Dynamic Invocation
invokedynamic byte code as the libraries that support this functionality have historically had security
problems and it is primarily needed only by scripting languages. In the future, this constraint will be eased to allow
for dynamic invocation in the specific lambda and string concatenation meta-factories used by Java code itself.
Disallow Native Methods
Forbids native methods as these provide the user access into operating system functionality such as file handling, network requests, general hardware interaction, threading, etc. These all constitute sources of non-determinism, and allowing such code to be called arbitrarily from the JVM would require deterministic guarantees on the native machine code level. This falls out of scope for the DJVM.
Disallow Finalizer Methods
Forbids finalizers as these can be called at unpredictable times during execution, given that their invocation is controlled by the garbage collector. As stated in the standard Java documentation:
Called by the garbage collector on an object when garbage collection determines that there are no more references to the object.
Disallow Overridden Sandbox Package
Forbids attempts to override rewritten classes. For instance, loading a class
com.foo.Bar into the sandbox,
analyses it, rewrites it and places it into
sandbox.com.foo.Bar. Attempts to place originating classes in the
sandbox package will therefore fail as this poses a security risk. Doing so would essentially bypass rule
validation and instrumentation.
For obvious reasons, the breakpoint operation code is forbidden as this can be exploited to unpredictably suspend code execution and consequently interfere with any time bounds placed on the execution.
For now, the use of reflection APIs is forbidden as the unmanaged use of these can provide means of breaking out of the protected sandbox environment.
Disallow Unsupported API Versions
Ensures that loaded classes are targeting an API version between 1.5 and 1.8 (inclusive). This is merely to limit the breadth of APIs from the standard runtime that needs auditing.
The runtime accountant inserts calls to an accounting object before expensive byte code. The goal of this rewrite is to deterministically terminate code that has run for an unacceptably long amount of time or used an unacceptable amount of memory. Types of expensive byte code include method invocation, memory allocation, branching and exception throwing.
The cost instrumentation strategy used is a simple one: just counting byte code that are known to be expensive to execute. The methods can be limited in size and jumps count towards the costing budget, allowing us to determine a consistent halting criteria. However it is still possible to construct byte code sequences by hand that take excessive amounts of time to execute. The cost instrumentation is designed to ensure that infinite loops are terminated and that if the cost of verifying a transaction becomes unexpectedly large (e.g., contains algorithms with complexity exponential in transaction size) that all nodes agree precisely on when to quit. It is not intended as a protection against denial of service attacks. If a node is sending you transactions that appear designed to simply waste your CPU time then simply blocking that node is sufficient to solve the problem, given the lack of global broadcast.
The budgets are separate per operation code type, so there is no unified cost model. Additionally the instrumentation is high overhead. A more sophisticated design would be to calculate byte code costs statically as much as possible ahead of time, by instrumenting only the entry point of ‘accounting blocks’, i.e., runs of basic blocks that end with either a method return or a backwards jump. Because only an abstract cost matters (this is not a profiler tool) and because the limits are expected to bet set relatively high, there is no need to instrument every basic block. Using the max of both sides of a branch is sufficient when neither branch target contains a backwards jump. This sort of design will be investigated if the per category budget accounting turns out to be insufficient.
A further complexity comes from the need to constrain memory usage. The sandbox imposes a quota on bytes allocated rather than bytes retained in order to simplify the implementation. This strategy is unnecessarily harsh on smart contracts that churn large quantities of garbage yet have relatively small peak heap sizes and, again, it may be that in practice a more sophisticated strategy that integrates with the garbage collector is required in order to set quotas to a usefully generic level.
Instrumentation and Rewriting
Always Use Strict Floating Point Arithmetic
strictfp flag on all methods, which requires the JVM to do floating point arithmetic in a hardware
independent fashion. Whilst we anticipate that floating point arithmetic is unlikely to feature in most smart contracts
(big integer and big decimal libraries are available), it is available for those who want to use it.
Always Use Exact Math
Replaces integer and long addition and multiplication with calls to
respectively. Further work can be done to implement exact operations for increments, decrements and subtractions as
well. These calls into
java.lang.Math essentially implement checked arithmetic over integers, which will throw an
exception if the operation overflows.
Always Inherit From Sandboxed Object
As mentioned further up,
Object.hashCode() is typically implemented using either the memory address of the object
or a random number; which are both non-deterministic. The DJVM shields the runtime from this source of non-determinism
by rewriting all classes that inherit from
java.lang.Object to derive from
Object implementation takes a hash-code as an input argument to the primary constructor, persists it
and returns the value from the
hashCode() method implementation. It also has an overridden implementation of
The loaded classes are further rewritten in two ways:
- All allocations of new objects of type
java.lang.Objectget mapped into using the sandboxed object.
- Calls to the constructor of
java.lang.Objectget mapped to the constructor of
sandbox.java.lang.Objectinstead, passing in a constant value for now. In the future, we can easily have this passed-in hash-code be a pseudo random number seeded with, for instance, the hash of the transaction or some other dynamic value, provided of course that it is deterministically derived.
Disable Synchronised Methods and Blocks
The DJVM doesn’t support multi-threading and so synchronised methods and code blocks have little use in sandboxed code. Consequently, we automatically transform them into ordinary methods and code blocks instead.
Further work is planned:
- To enable controlled use of reflection APIs.
- Currently, dynamic invocation is disallowed. Allow specific lambda and string concatenation meta-factories used by Java code itself.
- Map more mathematical operations to use their ‘exact’ counterparts.
- General tightening of the enforced constraints.
- Cost accounting of runtime metrics such as memory allocation, branching and exception handling. More specifically defining sensible runtime thresholds and make further improvements to the instrumentation.
- More sophisticated runtime accounting as discussed in Runtime Costing.
Open your terminal and navigate to the
djvm directory in the Corda source tree. Then issue the following command:
This will build the DJVM tool and install a shortcut on Bash-enabled systems. It will also generate a Bash completion
file and store it in the
shell folder. This file can be sourced from your Bash initialisation script.
$ cd ~ $ djvm
Now, you can create a new Java file from a skeleton that
djvm provides, compile the file, and consequently run it
by issuing the following commands:
$ djvm new Hello $ vim tmp/net/corda/sandbox/Hello.java $ djvm build Hello $ djvm run Hello
This run will produce some output similar to this:
Running class net.corda.sandbox.Hello... Execution successful - result = null Runtime Cost Summary: - allocations = 0 - invocations = 1 - jumps = 0 - throws = 0
The output should be pretty self-explanatory, but just to summarise:
- It prints out the return value from the
Function<Object, Object>.apply()method implemented in
- It also prints out the aggregated costs for allocations, invocations, jumps and throws.
Other commands to be aware of are:
djvm checkwhich allows you to perform some up-front static analysis without running the code. However, be aware that the DJVM also transforms some non-deterministic operations into
RuleViolationErrorexceptions. A successful
checktherefore does not guarantee that the code will behave correctly at runtime.
djvm inspectwhich allows you to inspect what byte code modifications will be applied to a class.
djvm showwhich displays the transformed byte code of a class, i.e., the end result and not the difference.