Missing Components
Assuming Geolog's core is mature and stable, what would be needed to make it production-ready?
Summary
Geolog has a solid core engine (parser, type checker, chase, tensor algebra) but is missing everything around it:
- No way in — no API, no language bindings
- No way to scale — no parallelism, no indexes, no query optimization
- No way to operate — no concurrency, no recovery, no monitoring
- No way to debug — no logging, no traces, no profiling
It's a well-built engine without a car around it.
1. Integration & APIs
| Missing |
Why It Matters |
| REST API |
Can't call Geolog from web apps, microservices, or other languages |
| Language bindings |
No Python, JavaScript, or FFI — Rust-only |
| LSP (Language Server) |
No IDE autocomplete, error squiggles, go-to-definition |
| JSON/YAML serialization |
Only binary format (rkyv) — can't inspect data externally |
| Async API |
All operations block — can't integrate with async runtimes |
What Integration Would Look Like
# This doesn't exist today
from geolog import Theory, Instance
theory = Theory.parse("""
theory Graph {
V : Sort;
E : Sort;
src : E -> V;
tgt : E -> V;
}
""")
instance = Instance.create(theory)
instance.add_element("V", "alice")
instance.add_element("V", "bob")
instance.add_element("E", "edge1")
instance.set_function("edge1", "src", "alice")
instance.set_function("edge1", "tgt", "bob")
# Run chase and get results as JSON
results = instance.chase()
print(results.to_json())
# REST API that doesn't exist
curl -X POST http://localhost:8080/chase \
-H "Content-Type: application/json" \
-d '{"instance": "MyGraph", "max_iterations": 100}'
2. Performance & Scale
| Missing |
Why It Matters |
| Cost-based query optimizer |
No cardinality estimates — can't choose optimal join order |
| Secondary indexes |
Only RoaringBitmaps — no B-trees for range queries |
| Parallel execution |
Single-threaded only |
| Benchmark suite |
No way to track performance regressions |
| Memory profiling |
No visibility into allocation patterns |
What Would Struggle
Scenario: Theory with 10,000 elements and 50 axioms
Problems:
→ No way to predict which axioms are expensive
→ No parallel chase execution
→ No index to speed up specific lookups
→ No benchmark to know if changes made it slower
What's Needed
// Cost-based optimizer (doesn't exist)
let plan = optimizer.compile(query);
println!("Estimated cost: {}", plan.estimated_cost());
println!("Join order: {:?}", plan.join_order());
// Parallel chase (doesn't exist)
let results = chase_parallel(axioms, structure, num_threads=4);
// Benchmarks (don't exist)
// benches/chase_benchmark.rs
// benches/tensor_benchmark.rs
3. Solver Intelligence
| Missing |
Why It Matters |
| Search heuristics |
Breadth-first only — no intelligent variable/value ordering |
| Backtracking |
Can't explore branches — only refines single partial model |
| Lemma learning |
No conflict-driven learning (CDCL) like modern SAT/SMT solvers |
| External prover integration |
Can't delegate to Z3, Lean, or Coq |
Current Behavior
Solver tries everything in order:
x = 1? Try it.
x = 2? Try it.
x = 3? Try it.
...
No learning from failures.
No "this variable is most constrained, try it first."
What Modern Solvers Do
CDCL (Conflict-Driven Clause Learning):
1. Try x = 1
2. Conflict detected!
3. Learn: "x ≠ 1" (add as constraint)
4. Backtrack and never try x = 1 again
Variable ordering:
1. Count constraints on each variable
2. Try most-constrained variable first
3. Fail fast, prune search space early
4. Reliability & Operations
| Missing |
Why It Matters |
| Multi-user concurrency |
No locking — can't have multiple writers |
| ACID transactions |
No rollback on failure |
| Write-ahead log (WAL) |
No crash recovery |
| Replication |
No distributed deployment |
| Garbage collection |
Tombstoned elements accumulate forever |
| Compression |
Data size grows unbounded |
Production Scenario That Fails
User A: :chase BigInstance (starts running)
User B: :add BigInstance x:V; (modifies while A is running)
Result: Race condition, possible data corruption
No way to recover if either crashes mid-operation
No way to rollback User B's change if it breaks something
What's Needed
// Transactions (don't exist)
let tx = store.begin_transaction();
tx.add_element("V", "new_vertex")?;
tx.chase("MyInstance")?;
tx.commit()?; // Or tx.rollback() on error
// Concurrency control (doesn't exist)
let lock = store.write_lock("MyInstance");
// ... safe modifications ...
drop(lock);
// Crash recovery (doesn't exist)
// WAL ensures operations are durable before acknowledging
5. Developer Experience
| Missing |
Why It Matters |
| Logging framework |
No structured logs for debugging |
| Interactive debugger |
Can't step through solver decisions |
| Execution traces |
Can't replay what happened |
| IDE plugin |
No syntax highlighting, no error squiggles |
| Tutorials |
Only reference docs, no guided learning |
Debugging Today
> :chase MyInstance
// Something went wrong... but what?
// No logs, no trace, just the final state
// Which axiom fired? Which elements were created? Unknown.
What's Needed
// Structured logging (doesn't exist)
[2026-03-19 10:30:01] INFO chase: Starting chase on MyInstance
[2026-03-19 10:30:01] DEBUG chase: Axiom ax/trans fired with {x: v1, y: v2, z: v3}
[2026-03-19 10:30:01] DEBUG chase: Added relation [x:v1, y:v3] leq
[2026-03-19 10:30:02] INFO chase: Fixpoint reached after 3 iterations
// Interactive debugger (doesn't exist)
> :debug chase MyInstance
Breakpoint at axiom ax/trans
Variables: x=v1, y=v2, z=v3
Action: Add [x:v1, y:v3] leq
(debug) step
(debug) inspect structure
(debug) continue
6. Error Handling
| Missing |
Why It Matters |
| Typed error enums |
All errors are strings — can't handle programmatically |
| Error recovery suggestions |
"Did you mean X?" doesn't exist |
| Partial results |
If 90% succeeds, you get nothing |
| Stack traces |
Limited context for where errors occur |
Current State
// All errors are just strings
fn elaborate_theory(...) -> Result<Theory, String>
// Can only display, not handle programmatically
match result {
Err(msg) => println!("{}", msg), // That's all you can do
}
What's Needed
enum GeologError {
Parse(ParseError),
Type(TypeError),
Chase(ChaseError),
Solver(SolverError),
}
enum ParseError {
UnexpectedToken { span: Span, found: Token, expected: Vec<Token> },
UnterminatedString { span: Span },
// ...
}
enum TypeError {
UndefinedSort { name: String, span: Span, similar: Vec<String> },
TypeMismatch { expected: Type, found: Type, span: Span },
// ...
}
// Now you can handle errors programmatically
match result {
Err(GeologError::Type(TypeError::UndefinedSort { name, similar, .. })) => {
println!("Unknown sort '{}'. Did you mean '{}'?", name, similar[0]);
}
// ...
}
7. Missing Language Features
| Missing |
Why It Matters |
| Modules/imports |
Can't organize large theories into files |
| Parameterized axioms |
Can't write generic rules that work across sorts |
| Arithmetic |
No x + 1 = y or count > 0 |
| Aggregation |
No count, sum, max over relations |
| Stratified negation |
No "if NOT X" even in limited safe form |
What You Can't Express
// Modules (don't exist)
import std/graph;
import std/preorder;
// Arithmetic (doesn't exist)
ax/increment : forall x : Nat. |- successor(x) = x + 1;
// Aggregation (doesn't exist)
ax/has_friends : forall p : Person.
count([f: f] friend_of(p, f)) > 0 |- popular(p);
// Stratified negation (doesn't exist)
ax/lonely : forall p : Person.
not exists f : Person. friend_of(p, f) |- lonely(p);
Priority Ranking
If making Geolog production-ready:
| Priority |
Component |
Effort |
Impact |
| 1 |
REST API |
Medium |
Unlocks all integrations |
| 2 |
LSP server |
Medium |
Makes language usable in IDEs |
| 3 |
Structured errors |
Low |
Enables better tooling |
| 4 |
Benchmark suite |
Low |
Enables performance work |
| 5 |
Logging/tracing |
Low |
Enables debugging |
| 6 |
Concurrency/locking |
High |
Required for multi-user |
| 7 |
Solver heuristics |
High |
Makes solver practical |
| 8 |
Cost-based optimizer |
High |
Enables scale |
| 9 |
Language bindings |
Medium |
Broader adoption |
| 10 |
Modules/imports |
Medium |
Enables large projects |
Maturity by Area
| Area |
Maturity |
What Exists |
Critical Gap |
| Query |
70% |
Chase, optimization, temporal ops |
No cost-based optimization |
| Solver |
65% |
Explicit tree, tactics framework |
No heuristics, no backtracking |
| Storage |
75% |
Append-only, versioning |
No concurrency, no recovery |
| API |
45% |
Clean Rust library |
No REST, no LSP, no FFI |
| Performance |
40% |
Fuzzing, property tests |
No benchmarks, no profiling |
| Debugging |
50% |
Error formatting, query plans |
No logging, no debugger |
| Errors |
60% |
Recoverable, source spans |
String-only, limited context |
| Docs |
65% |
Architecture, inline comments |
No IDE support, no tutorials |
