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Selector Service

The Selector Service (token/services/selector) picks the unspent tokens (UTXOs) that fund a transaction and holds them under a temporary lock while the transaction is assembled, so that concurrent transactions of the same wallet do not try to spend the same tokens.

Core Responsibilities

The Selector Service is responsible for:

• UTXO Selection: Finding a set of spendable tokens that cover the total quantity required for a transfer operation.
• Double-Spending Mitigation: Temporarily locking selected tokens during the transaction assembly phase to prevent multiple concurrent transactions from attempting to spend the same tokens.
• Candidate Enumeration: Walking the wallet’s candidate tokens in randomized order, locking each one as it is encountered, and stopping as soon as the accumulated amount covers the request. Token amounts do not order or rank the candidates.

Interaction with TTX and Storage

The Selector Service bridges the gap between the high-level TTX Service and the internal TokenDB.

graph LR
    TTX[TTX Service] --> Selector[Selector Service]
    
    subgraph "Token Fetcher"
        Fetcher[Fetcher Logic]
        Fetcher -->|Cache Hit| Cache[Cache]
        Fetcher -->|Cache Miss| TokenDB[Token Store - TokenDB]
    end
    
    subgraph "Selection Logic"
        Query[Query Spendable Tokens]
        Pick[Take Next Candidate - randomized order]
        Lock[Acquire Temporary Lock]
        Done[Return Locked Tokens]
    end
    
    Selector --> Fetcher
    Selector --> Query
    Query --> Pick
    Pick --> Lock
    Lock -->|locked by another process, or sum still below target| Pick
    Lock -->|requested amount covered| Done

How the components interact:

• Selector Service: Creates a selector instance per transaction and orchestrates the Selection Logic steps
• Query Spendable Tokens: Selector calls the Fetcher to retrieve available tokens
• Fetcher Logic: Checks cache first (fast path), queries Token Store - TokenDB on cache miss (slow path)
• Take Next Candidate: Selector takes the next token from the randomized candidate set; the token’s amount plays no part in the choice
• Acquire Temporary Lock: Selector locks each candidate as it is encountered, before it knows whether the request can be covered at all; a candidate already locked by another process is skipped and the loop moves on

Key Components

Selector Manager

The SelectorManager is the entry point for obtaining a Selector instance anchored to a specific transaction. It ensures that the selection process is consistent and tied to the lifecycle of a single token request.

Token Selection Algorithm

Selection is a randomized greedy first-fit. It is not configurable, and it is not amount-aware. Selector.selectInternal (token/services/selector/sherdlock/selector.go) does the following:

1. the candidate tokens of the wallet and token type are enumerated in randomized order,
2. each candidate is locked as it is encountered — a candidate already locked by another process is skipped; a lock failure wrapping token.SelectorRateLimited is a hard abort (not a skip),
3. the amounts of the successfully locked tokens are added up, and
4. the selector returns as soon as the running sum reaches the requested quantity.

A token’s amount therefore only decides when the loop stops, never which candidate is picked. Two consequences worth planning for:

• The number and size of the inputs is not minimized. A request that a single large token could have covered may well be funded by several small ones.
• The result is not deterministic. The same request against the same wallet can select a different set of tokens, and a different number of inputs, on each run.

The randomization is deliberate. It is what spreads concurrent selectors of the same wallet across different candidates: walking a fixed order would make every selector contend for the same first tokens, driving up lock failures and, with them, the immediate-retry path that gives up with token.SelectorSufficientButLockedFunds, and beyond it the backoff path that ends in token.SelectorInsufficientFunds.

The shuffle lives in the sherdlock fetcher, not in the selection loop (token/services/selector/sherdlock/fetcher.go): the lazy fetcher wraps the database iterator in collections.NewPermutatedIterator, and the cached fetcher hands out a fresh permutation of the cached slice on every query. The simple driver does not shuffle — it walks the database iterator in the order the token store returns it (token/services/selector/simple/selector.go) — so concurrent selectors under simple are more exposed to colliding on the same leading candidates.

How it works in the flow (see “Selection Logic” subgraph in diagram):

1. TTX Request: TTX Service requests token selection for a transfer operation
2. Query Spendable Tokens: Selector queries via Fetcher (Cache Hit → fast path, Cache Miss → Token Store - TokenDB)
3. Take Next Candidate: Selector takes the next token from the randomized candidate set
4. Acquire Temporary Lock: The candidate is locked to prevent double-spending (in the TokenLocks table under the sherdlock driver, in memory under simple); on success its amount is added to the running sum, on failure the loop moves to the next candidate
5. Return or Retry: The selector returns as soon as the sum covers the request; if the candidate set is exhausted while other processes hold locks, it retries in two distinct layers:
   • Immediate-retry layer (sherdlock only): the inner loop refetches — refreshing the sherdlock token cache via the fetcher — up to a hardcoded maxImmediateRetries = 5 times without releasing its already-acquired locks, then gives up with token.SelectorSufficientButLockedFunds. Under simple, there is no equivalent cache layer; the outer retry loop re-queries the query service directly on every attempt.
   • Backoff layer: a configurable numRetries / retryInterval outer loop (the StubbornSelector wrapper in sherdlock; the numRetry / timeout loop in simple) releases locks, sleeps, and re-runs the whole selection from scratch. Exhausting this layer returns token.SelectorInsufficientFunds.

Strategies that are not implemented

Amount-aware strategies — smallest-first, largest-first, First-In-First-Out, or minimizing the number of inputs — are not implemented and cannot be configured. There is no strategy abstraction in the code and no configuration key that selects one. Making selection amount-aware is tracked in issue #2017.

Locking Mechanism

To prevent double-spending before the transaction is committed to the ledger, the Selector Service uses a local TokenLocks table in the Storage Service (see “TokenLocks” box in diagram above).

Lock lifecycle:

1. Lock Acquisition: When the selector takes a candidate token, it attempts to insert a record in the TokenLocks table.
2. Concurrency Control: If another concurrent process has already locked that token, the insertion fails, and the selector moves on to the next candidate.
3. Lock Release: Locks are released either when the transaction reaches finality (success/failure) or when a timeout occurs, ensuring that tokens do not remain permanently inaccessible due to crashed or abandoned transactions.

In-Memory Locker Internals

The simple driver keeps its locks in memory (token/services/selector/simple/inmemory) instead of the TokenLocks table. Its state is sharded per owner (the wallet the tokens are selected for): every owner has its own shard, holding that owner’s locked tokens behind its own mutex, and the shards themselves live in a registry map behind a second mutex. Two owners therefore never serialize against each other, not even while a lock attempt is waiting on a transaction-status lookup.

Two invariants keep the two mutex levels safe:

• Lock order is shard first, registry second. The only place that takes the registry lock while holding a shard lock is the pruning of an empty shard, which must observe the shard as empty while holding it. Every operation that needs to walk all shards (IsLocked, UnlockByTxID, the background collector, the locked-token count) therefore snapshots the registry, releases the registry lock, and only then takes the individual shard locks. Taking the two in the opposite order deadlocks the locker.
• A pruned shard is never written to. When a shard becomes empty it is removed from the registry and marked as pruned. A Lock that had already obtained that shard re-checks the mark under the shard lock and retries on the freshly registered shard, so a lock can never end up in a shard no other operation can reach. Pruning also removes the registry entry only if it still points at that exact shard, so a stale empty shard cannot evict a newer shard holding live locks.

The background collector (the goroutine that frees locks of finalized transactions) copies a shard’s entries, releases the shard lock, and only then looks the transaction statuses up, so a slow status provider never blocks locking or unlocking. Because the shard is unlocked in between, each entry is re-validated before removal — same transaction ID and same last-access time — and entries that were reclaimed or re-accessed meanwhile are kept.

Token Fetcher and Cache

The selector uses a Token Fetcher to retrieve available tokens from the database. The fetcher uses a Ristretto LRU cache to improve performance by caching token queries (keyed by wallet+currency).

Flow: Selector.Select() → Fetcher.UnspentTokensIteratorBy(wallet, currency) → Token Iterator

How it works:

1. Selector requests tokens from Fetcher for a specific wallet and currency
2. Fetcher checks its cache (keyed by wallet+currency)
3. If cache is fresh, returns cached tokens immediately (fast path)
4. If cache is stale, queries database and updates cache
5. Selector iterates through tokens, attempting to lock each one
6. If insufficient tokens, selector requests fresh data and retries

Adaptive refresh strategy with two triggers:

• Time-based: Refreshes when data is older than fetcherCacheRefresh
• Query-based: Refreshes after fetcherCacheMaxQueries queries to prevent serving stale data in high-throughput scenarios

Configuration

Configure the selector service in your core.yaml:

token:
  selector:
    driver: sherdlock                    # Selector implementation and locking backend: sherdlock | simple (default: sherdlock)
    numRetries: 3                        # Retry attempts for token selection (default: 3)
    retryInterval: 5s                    # Wait time between retries (default: 5s)
    leaseExpiry: 3m                      # Lock expiration time (default: 3m)
    leaseCleanupTickPeriod: 1m           # Lock cleanup interval (default: 1m)
    fetcherCacheSize: 1000               # Cache size in entries (default: 0 = use fetcher default)
    fetcherCacheRefresh: 30s             # Cache refresh interval (default: 0 = use fetcher default)
    fetcherCacheMaxQueries: 100          # Max queries before cache refresh (default: 0 = use fetcher default)

Driver

driver selects the selector implementation and, with it, the locking backend:

• sherdlock (default): locks in the TokenLocks table of the Storage Service, with leases governed by leaseExpiry and leaseCleanupTickPeriod.
• simple: keeps its locks in memory (see In-Memory Locker Internals).

It does not select a selection algorithm: both drivers walk candidates greedily and stop on first cover, but they diverge in several ways beyond the shuffle:

• sherdlock randomizes the candidate order; simple walks tokens in database order.
• sherdlock holds already-acquired locks across immediate retries; simple releases all locks between every retry attempt.
• simple runs a GetTokens concurrency check after a successful cover and can return a fourth error sentinel, token.SelectorSufficientFundsButConcurrencyIssue, which sherdlock does not produce.

Cache Configuration

The fetcher cache improves performance by caching token queries:

• fetcherCacheSize: Maximum number of cached query results. Set to 0 to use the fetcher’s default size.
• fetcherCacheRefresh: Time interval after which cached data is considered stale and refreshed. Set to 0 to use the fetcher’s default interval.
• fetcherCacheMaxQueries: Maximum number of queries before forcing a cache refresh. Set to 0 to use the fetcher’s default limit.

Example: With fetcherCacheSize: 1000, fetcherCacheRefresh: 30s, and fetcherCacheMaxQueries: 100, the cache stores up to 1000 query results, refreshes data every 30 seconds, and forces a refresh after 100 queries.

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