TidesDB C++ API Reference

If you want to download the source of this document, you can find it here.

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Getting Started

Prerequisites

You must have the TidesDB shared C library installed on your system. You can find the installation instructions here.

Installation

git clone https://github.com/tidesdb/tidesdb-cpp.git
cd tidesdb-cpp
cmake -S . -B build
cmake --build build
sudo cmake --install build

Custom Installation Paths

If you installed TidesDB to a non-standard location, you can specify custom paths:

cmake -S . -B build -DCMAKE_PREFIX_PATH=/custom/path
cmake --build build

Usage

Opening and Closing a Database

#include <tidesdb/tidesdb.hpp>
#include <iostream>

int main() {
    tidesdb::Config config;
    config.dbPath = "./mydb";
    config.numFlushThreads = 2;                    // Flush thread pool size (default: 2)
    config.numCompactionThreads = 2;               // Compaction thread pool size (default: 2)
    config.logLevel = tidesdb::LogLevel::Info;      // Log level (default: Info)
    config.blockCacheSize = 64 * 1024 * 1024;      // 64MB global block cache (default: 64MB)
    config.maxOpenSSTables = 256;                   // Max cached SSTable structures (default: 256)
    config.maxMemoryUsage = 0;                      // Global memory limit in bytes (default: 0 = auto, 50% of system RAM)
    config.logToFile = false;                       // Write logs to file instead of stderr (default: false)
    config.logTruncationAt = 24 * 1024 * 1024;     // Log file truncation size (default: 24MB), 0 = no truncation

    try {
        tidesdb::TidesDB db(config);
        std::cout << "Database opened successfully" << std::endl;
        } catch (const tidesdb::Exception& e) {
        std::cerr << "Failed to open database: " << e.what() << std::endl;
        return 1;
    }

    return 0;
}

Using default configuration

Use TidesDB::defaultConfig() to get a configuration with sensible defaults, then override specific fields as needed:

auto config = tidesdb::TidesDB::defaultConfig();
config.dbPath = "./mydb";
config.logLevel = tidesdb::LogLevel::Warn;  // Override: only warnings and errors

tidesdb::TidesDB db(config);

Logging

TidesDB provides structured logging with multiple severity levels.

Log Levels

• LogLevel::Debug · Detailed diagnostic information
• LogLevel::Info · General informational messages (default)
• LogLevel::Warn · Warning messages for potential issues
• LogLevel::Error · Error messages for failures
• LogLevel::Fatal · Critical errors that may cause shutdown
• LogLevel::None · Disable all logging

Configure at startup

tidesdb::Config config;
config.dbPath = "./mydb";
config.logLevel = tidesdb::LogLevel::Debug;  // Enable debug logging

tidesdb::TidesDB db(config);

Production configuration

tidesdb::Config config;
config.dbPath = "./mydb";
config.logLevel = tidesdb::LogLevel::Warn;  // Only warnings and errors

tidesdb::TidesDB db(config);

Output format Logs are written to stderr by default with timestamps:

[HH:MM:SS.mmm] [LEVEL] filename:line: message

Log to file

Enable logToFile to write logs to a LOG file in the database directory instead of stderr:

tidesdb::Config config;
config.dbPath = "./mydb";
config.logLevel = tidesdb::LogLevel::Debug;
config.logToFile = true;  // Write to ./mydb/LOG instead of stderr

tidesdb::TidesDB db(config);
// Logs are now written to ./mydb/LOG

The log file is opened in append mode and uses line buffering for real-time logging. If the log file cannot be opened, logging falls back to default.

Creating and Dropping Column Families

Column families are isolated key-value stores with independent configuration.

auto cfConfig = tidesdb::ColumnFamilyConfig::defaultConfig();
db.createColumnFamily("my_cf", cfConfig);

auto cfConfig = tidesdb::ColumnFamilyConfig::defaultConfig();
cfConfig.writeBufferSize = 128 * 1024 * 1024;
cfConfig.levelSizeRatio = 10;
cfConfig.minLevels = 5;
cfConfig.compressionAlgorithm = tidesdb::CompressionAlgorithm::LZ4;
cfConfig.enableBloomFilter = true;
cfConfig.bloomFPR = 0.01;
cfConfig.enableBlockIndexes = true;
cfConfig.syncMode = tidesdb::SyncMode::Interval;
cfConfig.syncIntervalUs = 128000;
cfConfig.defaultIsolationLevel = tidesdb::IsolationLevel::ReadCommitted;
cfConfig.useBtree = false;  // Use block-based format (default), set true for B+tree klog format

db.createColumnFamily("my_cf", cfConfig);

db.dropColumnFamily("my_cf");

CRUD Operations

All operations in TidesDB are performed through transactions for ACID guarantees.

Writing Data

auto cf = db.getColumnFamily("my_cf");

auto txn = db.beginTransaction();
txn.put(cf, "key", "value", -1);
txn.commit();

Writing with TTL

#include <ctime>

auto cf = db.getColumnFamily("my_cf");

auto txn = db.beginTransaction();

auto ttl = std::time(nullptr) + 10;

txn.put(cf, "temp_key", "temp_value", ttl);
txn.commit();

TTL Examples

auto ttl = static_cast<std::time_t>(-1);

auto ttl = std::time(nullptr) + (5 * 60);

auto ttl = std::time(nullptr) + (60 * 60);

auto ttl = static_cast<std::time_t>(1735689599);

Reading Data

auto cf = db.getColumnFamily("my_cf");

auto txn = db.beginTransaction();
auto value = txn.get(cf, "key");
std::string valueStr(value.begin(), value.end());
std::cout << "Value: " << valueStr << std::endl;

Deleting Data

auto cf = db.getColumnFamily("my_cf");

auto txn = db.beginTransaction();
txn.del(cf, "key");
txn.commit();

Multi-Operation Transactions

auto cf = db.getColumnFamily("my_cf");

auto txn = db.beginTransaction();

txn.put(cf, "key1", "value1", -1);
txn.put(cf, "key2", "value2", -1);
txn.del(cf, "old_key");

txn.commit();

Transaction Rollback

auto cf = db.getColumnFamily("my_cf");

auto txn = db.beginTransaction();
txn.put(cf, "key", "value", -1);

// Discard all operations
txn.rollback();

Iterating Over Data

Iterators provide efficient bidirectional traversal over key-value pairs.

Forward Iteration

auto cf = db.getColumnFamily("my_cf");

auto txn = db.beginTransaction();
auto iter = txn.newIterator(cf);

iter.seekToFirst();

while (iter.valid()) {
    auto key = iter.key();
    auto value = iter.value();

    std::string keyStr(key.begin(), key.end());
    std::string valueStr(value.begin(), value.end());

    std::cout << "Key: " << keyStr << ", Value: " << valueStr << std::endl;

    iter.next();
}

Backward Iteration

auto cf = db.getColumnFamily("my_cf");

auto txn = db.beginTransaction();
auto iter = txn.newIterator(cf);

iter.seekToLast();

while (iter.valid()) {
    auto key = iter.key();
    auto value = iter.value();
    iter.prev();
}

Seeking

How Seek Works

With Block Indexes Enabled (enableBlockIndexes = true):

• Uses compact block index with parallel arrays (min/max key prefixes and file positions)
• Binary search through sampled keys at configurable ratio (default 1:1 via indexSampleRatio, meaning every block is indexed)
• Jumps directly to the target block using the file position
• Scans forward from that block to find the exact key
• Performance · O(log n) binary search + O(k) entries per block scan

Block indexes provide dramatic speedup for large SSTables at the cost of ~2-5% storage overhead for the compact index structure.

seek(key) · Positions iterator at the first key >= target key

auto iter = txn.newIterator(cf);

iter.seek("user:1000");

// Iterator is now positioned at "user:1000" or the next key after it
if (iter.valid()) {
    auto key = iter.key();
    std::string keyStr(key.begin(), key.end());
    std::cout << "Found: " << keyStr << std::endl;
}

seekForPrev(key) · Positions iterator at the last key <= target key

auto iter = txn.newIterator(cf);

iter.seekForPrev("user:2000");

// Iterator is now positioned at "user:2000" or the previous key before it
while (iter.valid()) {
    // Iterate backwards from this point
    iter.prev();
}

Prefix Seeking

Since seek positions the iterator at the first key >= target, you can use a prefix as the seek target to efficiently scan all keys sharing that prefix:

auto iter = txn.newIterator(cf);

std::string prefix = "user:";
iter.seek(prefix);

while (iter.valid()) {
    auto key = iter.key();
    std::string keyStr(key.begin(), key.end());

    // Stop when keys no longer match prefix
    if (keyStr.substr(0, prefix.size()) != prefix) break;

    auto value = iter.value();
    std::string valueStr(value.begin(), value.end());
    std::cout << "Found: " << keyStr << " = " << valueStr << std::endl;

    iter.next();
}

This pattern works across both memtables and SSTables. When block indexes are enabled, the seek operation uses binary search to jump directly to the relevant block, making prefix scans efficient even on large datasets.

Getting Column Family Statistics

Retrieve detailed statistics about a column family.

auto cf = db.getColumnFamily("my_cf");

auto stats = cf.getStats();

std::cout << "Number of Levels: " << stats.numLevels << std::endl;
std::cout << "Memtable Size: " << stats.memtableSize << " bytes" << std::endl;
std::cout << "Total Keys: " << stats.totalKeys << std::endl;
std::cout << "Total Data Size: " << stats.totalDataSize << " bytes" << std::endl;
std::cout << "Average Key Size: " << stats.avgKeySize << " bytes" << std::endl;
std::cout << "Average Value Size: " << stats.avgValueSize << " bytes" << std::endl;
std::cout << "Read Amplification: " << stats.readAmp << std::endl;
std::cout << "Cache Hit Rate: " << stats.hitRate << std::endl;

// B+tree stats (only populated if useBtree=true)
if (stats.useBtree) {
    std::cout << "B+tree Total Nodes: " << stats.btreeTotalNodes << std::endl;
    std::cout << "B+tree Max Height: " << stats.btreeMaxHeight << std::endl;
    std::cout << "B+tree Avg Height: " << stats.btreeAvgHeight << std::endl;
}

// Per-level statistics
for (int i = 0; i < stats.numLevels; ++i) {
    std::cout << "Level " << i << ": "
              << stats.levelNumSSTables[i] << " SSTables, "
              << stats.levelSizes[i] << " bytes, "
              << stats.levelKeyCounts[i] << " keys" << std::endl;
}

if (stats.config.has_value()) {
    std::cout << "Write Buffer Size: " << stats.config->writeBufferSize << std::endl;
    std::cout << "Compression: " << static_cast<int>(stats.config->compressionAlgorithm) << std::endl;
    std::cout << "Bloom Filter: " << (stats.config->enableBloomFilter ? "enabled" : "disabled") << std::endl;
}

Statistics Fields

• numLevels · Number of LSM levels
• memtableSize · Current memtable size in bytes
• levelSizes · Total bytes per level
• levelNumSSTables · Number of SSTables per level
• totalKeys · Total number of keys across memtable and all SSTables
• totalDataSize · Total data size (klog + vlog) across all SSTables
• avgKeySize · Average key size in bytes
• avgValueSize · Average value size in bytes
• levelKeyCounts · Number of keys per level
• readAmp · Read amplification (point lookup cost multiplier)
• hitRate · Cache hit rate (0.0 if cache disabled)
• useBtree · Whether column family uses B+tree klog format
• btreeTotalNodes · Total B+tree nodes across all SSTables (only if useBtree=true)
• btreeMaxHeight · Maximum tree height across all SSTables (only if useBtree=true)
• btreeAvgHeight · Average tree height across all SSTables (only if useBtree=true)
• config · Column family configuration (optional)

Listing Column Families

auto cfList = db.listColumnFamilies();

std::cout << "Available column families:" << std::endl;
for (const auto& name : cfList) {
    std::cout << "  - " << name << std::endl;
}

Renaming a Column Family

Atomically rename a column family and its underlying directory.

db.renameColumnFamily("old_name", "new_name");

Behavior

• Waits for any in-progress flush or compaction to complete
• Atomically renames the column family directory on disk
• Updates all internal paths (SSTables, manifest, config)
• Thread-safe with proper locking

Cloning a Column Family

Create a complete copy of an existing column family with a new name. The clone contains all the data from the source at the time of cloning.

db.cloneColumnFamily("source_cf", "cloned_cf");

// Both column families now exist independently
auto original = db.getColumnFamily("source_cf");
auto clone = db.getColumnFamily("cloned_cf");

Behavior

• Flushes the source column family’s memtable to ensure all data is on disk
• Waits for any in-progress flush or compaction to complete
• Copies all SSTable files (.klog and .vlog) to the new directory
• Copies manifest and configuration files
• The clone is completely independent — modifications to one do not affect the other

Use cases

• Testing · Create a copy of production data for testing without affecting the original
• Branching · Create a snapshot of data before making experimental changes
• Migration · Clone data before schema or configuration changes
• Backup verification · Clone and verify data integrity without modifying the source

Compaction

Manual Compaction

auto cf = db.getColumnFamily("my_cf");

cf.compact();

Manual Memtable Flush

auto cf = db.getColumnFamily("my_cf");

cf.flushMemtable();

Checking Flush/Compaction Status

Check if a column family currently has flush or compaction operations in progress.

auto cf = db.getColumnFamily("my_cf");

if (cf.isFlushing()) {
    std::cout << "Flush in progress" << std::endl;
}

if (cf.isCompacting()) {
    std::cout << "Compaction in progress" << std::endl;
}

Use cases

• Graceful shutdown · Wait for background operations to complete before closing
• Maintenance windows · Check if operations are running before triggering manual compaction
• Monitoring · Track background operation status for observability

Updating Runtime Configuration

Update runtime-safe configuration settings without restarting the database.

auto cf = db.getColumnFamily("my_cf");

auto newConfig = tidesdb::ColumnFamilyConfig::defaultConfig();
newConfig.writeBufferSize = 256 * 1024 * 1024;
newConfig.skipListMaxLevel = 16;
newConfig.bloomFPR = 0.001;  // 0.1% false positive rate

bool persistToDisk = true;  // Save to config.ini
cf.updateRuntimeConfig(newConfig, persistToDisk);

Updatable settings (all applied by updateRuntimeConfig):

• writeBufferSize · Memtable flush threshold
• skipListMaxLevel · Skip list level for new memtables
• skipListProbability · Skip list probability for new memtables
• bloomFPR · False positive rate for new SSTables
• enableBloomFilter · Enable/disable bloom filters for new SSTables
• enableBlockIndexes · Enable/disable block indexes for new SSTables
• blockIndexPrefixLen · Block index prefix length for new SSTables
• indexSampleRatio · Index sampling ratio for new SSTables
• compressionAlgorithm · Compression for new SSTables (existing SSTables retain their original compression)
• klogValueThreshold · Value log threshold for new writes
• syncMode · Durability mode. Also updates the active WAL’s sync mode immediately
• syncIntervalUs · Sync interval in microseconds (only used when syncMode is SyncMode::Interval)
• levelSizeRatio · LSM level sizing (DCA recalculates capacities dynamically)
• minLevels · Minimum LSM levels
• dividingLevelOffset · Compaction dividing level offset
• l1FileCountTrigger · L1 file count compaction trigger
• l0QueueStallThreshold · Backpressure stall threshold
• defaultIsolationLevel · Default transaction isolation level
• minDiskSpace · Minimum disk space required
• commitHookFn / commitHookCtx · Commit hook callback and context

Non-updatable settings (not modified by this function):

• comparatorName · Cannot change sort order after creation (would corrupt key ordering in existing SSTables)
• useBtree · Cannot change klog format after creation (existing SSTables use the original format)

Changes apply immediately to new operations. Existing SSTables and memtables retain their original settings. The update operation is thread-safe.

Commit Hook (Change Data Capture)

ColumnFamily::setCommitHook registers a callback that fires synchronously after every transaction commit on a column family. The hook receives the full batch of committed operations atomically, enabling real-time change data capture without WAL parsing or external log consumers.

auto cf = db.getColumnFamily("my_cf");

// Define a commit hook
auto myHook = [](const tidesdb_commit_op_t* ops, int num_ops,
                 uint64_t commit_seq, void* ctx) -> int {
    for (int i = 0; i < num_ops; ++i) {
        std::string key(reinterpret_cast<const char*>(ops[i].key), ops[i].key_size);
        if (ops[i].is_delete) {
            std::cout << "[" << commit_seq << "] DELETE " << key << std::endl;
        } else {
            std::string value(reinterpret_cast<const char*>(ops[i].value), ops[i].value_size);
            std::cout << "[" << commit_seq << "] PUT " << key << " = " << value << std::endl;
        }
    }
    return 0;
};

// Attach hook at runtime
cf.setCommitHook(myHook, nullptr);

// Normal writes now trigger the hook automatically
auto txn = db.beginTransaction();
txn.put(cf, "user:1", "Alice", -1);
txn.commit();  // myHook fires here

// Detach hook
cf.clearCommitHook();

Setting hook via config at creation time

auto cfConfig = tidesdb::ColumnFamilyConfig::defaultConfig();
cfConfig.commitHookFn = myHook;
cfConfig.commitHookCtx = nullptr;

db.createColumnFamily("replicated_cf", cfConfig);

Callback signature

int (*tidesdb_commit_hook_fn)(const tidesdb_commit_op_t* ops, int num_ops,
                               uint64_t commit_seq, void* ctx);

The callback returns 0 on success. A non-zero return is logged as a warning but does not roll back the commit.

Operation struct fields (tidesdb_commit_op_t)

• key / key_size · Key bytes (valid only during callback)
• value / value_size · Value bytes (NULL / 0 for deletes, valid only during callback)
• ttl · Time-to-live for the entry
• is_delete · 1 for delete, 0 for put

Behavior

• The hook fires after WAL write, memtable apply, and commit status marking are complete — data is fully durable before the callback runs
• Hook failure (non-zero return) is logged but does not affect the commit result
• Each column family has its own independent hook; a multi-CF transaction fires the hook once per CF with only that CF’s operations
• commit_seq is monotonically increasing across commits and can be used as a replication cursor
• Pointers in tidesdb_commit_op_t are valid only during the callback invocation — copy any data you need to retain
• The hook executes synchronously on the committing thread; keep the callback fast to avoid stalling writers
• Setting the hook to NULL via clearCommitHook() disables it immediately with no restart required

Use cases

• Replication · Ship committed batches to replicas in commit order
• Event streaming · Publish mutations to Kafka, NATS, or any message broker
• Secondary indexing · Maintain a reverse index or materialized view
• Audit logging · Record every mutation with key, value, TTL, and sequence number
• Debugging · Attach a temporary hook in production to inspect live writes

The commitHookFn and commitHookCtx config fields are not persisted to config.ini. After a database restart, hooks must be re-registered by the application. This is by design — function pointers cannot be serialized.

Backup

Create an on-disk snapshot of an open database without blocking normal reads/writes.

db.backup("./mydb_backup");

Behavior

• Requires dir to be a non-existent directory or an empty directory
• Does not copy the LOCK file, so the backup can be opened normally
• Two-phase copy approach:
  • Copies immutable files first (SSTables listed in the manifest plus metadata/config files)
  • Forces memtable flushes, waits for flush/compaction queues to drain, then copies remaining files
• Database stays open and usable during backup

Checkpoint

Create a lightweight, near-instant snapshot of an open database using hard links instead of copying SSTable data.

db.checkpoint("./mydb_checkpoint");

Behavior

• Requires dir to be a non-existent directory or an empty directory
• For each column family:
  • Flushes the active memtable so all data is in SSTables
  • Halts compactions to ensure a consistent view of live SSTable files
  • Hard links all SSTable files (.klog and .vlog) into the checkpoint directory
  • Copies small metadata files (manifest, config) into the checkpoint directory
  • Resumes compactions
• Falls back to file copy if hard linking fails (e.g., cross-filesystem)
• Database stays open and usable during checkpoint

Checkpoint vs Backup

	backup	checkpoint
Speed	Copies every SSTable byte-by-byte	Near-instant (hard links, O(1) per file)
Disk usage	Full independent copy	No extra disk until compaction removes old SSTables
Portability	Can be moved to another filesystem or machine	Same filesystem only (hard link requirement)
Use case	Archival, disaster recovery, remote shipping	Fast local snapshots, point-in-time reads, streaming backups

Notes

• The checkpoint can be opened as a normal TidesDB database with TidesDB(config)
• Hard-linked files share storage with the live database. Deleting the original database does not affect the checkpoint (hard link semantics)

Block Cache Statistics

Get statistics for the global block cache (shared across all column families).

auto cacheStats = db.getCacheStats();

if (cacheStats.enabled) {
    std::cout << "Cache enabled: yes" << std::endl;
    std::cout << "Total entries: " << cacheStats.totalEntries << std::endl;
    std::cout << "Total bytes: " << cacheStats.totalBytes << std::endl;
    std::cout << "Hits: " << cacheStats.hits << std::endl;
    std::cout << "Misses: " << cacheStats.misses << std::endl;
    std::cout << "Hit rate: " << (cacheStats.hitRate * 100.0) << "%" << std::endl;
    std::cout << "Partitions: " << cacheStats.numPartitions << std::endl;
} else {
    std::cout << "Cache disabled (block_cache_size = 0)" << std::endl;
}

Cache statistics fields

• enabled · Whether block cache is active
• totalEntries · Number of cached blocks
• totalBytes · Total memory used by cached blocks
• hits · Number of cache hits
• misses · Number of cache misses
• hitRate · Hit rate as a decimal (0.0 to 1.0)
• numPartitions · Number of cache partitions

Range Cost Estimation

Estimate the computational cost of iterating between two keys in a column family. The returned value is an opaque double — meaningful only for comparison with other values from the same function. It uses only in-memory metadata and performs no disk I/O.

auto cf = db.getColumnFamily("my_cf");

double costA = cf.rangeCost("user:0000", "user:0999");
double costB = cf.rangeCost("user:1000", "user:1099");

if (costA < costB) {
    std::cout << "Range A is cheaper to iterate" << std::endl;
}

Key order does not matter — the function normalizes the range so keyA > keyB produces the same result as keyB > keyA.

How it works

• With block indexes enabled · Uses O(log B) binary search per overlapping SSTable to estimate block span
• Without block indexes · Falls back to byte-level key interpolation against SSTable min/max keys
• B+tree SSTables (useBtree=true) · Uses key interpolation against tree node counts, plus tree height as a seek cost
• Compressed SSTables receive a 1.5× weight multiplier for decompression overhead
• Each overlapping SSTable adds a small fixed cost for merge-heap operations
• The active memtable’s entry count contributes a small in-memory cost

Use cases

• Query planning · Compare candidate key ranges to find the cheapest one to scan
• Load balancing · Distribute range scan work across threads by estimating per-range cost
• Adaptive prefetching · Decide how aggressively to prefetch based on range size
• Monitoring · Track how data distribution changes across key ranges over time

A cost of 0.0 means no overlapping SSTables or memtable entries were found for the range.

Sync Modes

Control the durability vs performance tradeoff with three sync modes.

auto cfConfig = tidesdb::ColumnFamilyConfig::defaultConfig();

// Fastest, least durable (OS handles flushing)
cfConfig.syncMode = tidesdb::SyncMode::None;

// Balanced performance with periodic background syncing
cfConfig.syncMode = tidesdb::SyncMode::Interval;
cfConfig.syncIntervalUs = 128000;  // Sync every 128ms (default)

// Most durable (fsync on every write)
cfConfig.syncMode = tidesdb::SyncMode::Full;

db.createColumnFamily("my_cf", cfConfig);

Sync Mode Details

• SyncMode::None · No explicit sync, relies on OS page cache (fastest, least durable)

  • Best for · Maximum throughput, acceptable data loss on crash
  • Use case · Caches, temporary data, reproducible workloads

• SyncMode::Interval · Periodic background syncing at configurable intervals (balanced)

  • Best for · Production workloads requiring good performance with bounded data loss
  • Use case · Most applications, configurable durability window
  • Single background sync thread monitors all column families using interval mode
  • Structural operations (flush, compaction, WAL rotation) always enforce durability
  • At most syncIntervalUs worth of data at risk on crash

• SyncMode::Full · Fsync on every write operation (slowest, most durable)

  • Best for · Critical data requiring maximum durability
  • Use case · Financial transactions, audit logs, critical metadata

Sync Interval Examples

// Sync every 100ms (good for low-latency requirements)
cfConfig.syncMode = tidesdb::SyncMode::Interval;
cfConfig.syncIntervalUs = 100000;

// Sync every 128ms (default)
cfConfig.syncMode = tidesdb::SyncMode::Interval;
cfConfig.syncIntervalUs = 128000;

// Sync every 1 second (higher throughput, more data at risk)
cfConfig.syncMode = tidesdb::SyncMode::Interval;
cfConfig.syncIntervalUs = 1000000;

Regardless of sync mode, TidesDB always enforces durability for structural operations: memtable flush to SSTable, SSTable compaction and merging, WAL rotation, and column family metadata updates.

Compression Algorithms

TidesDB supports multiple compression algorithms:

auto cfConfig = tidesdb::ColumnFamilyConfig::defaultConfig();

cfConfig.compressionAlgorithm = tidesdb::CompressionAlgorithm::None;     // No compression
cfConfig.compressionAlgorithm = tidesdb::CompressionAlgorithm::LZ4;      // LZ4 standard (default)
cfConfig.compressionAlgorithm = tidesdb::CompressionAlgorithm::LZ4Fast;  // LZ4 fast mode
cfConfig.compressionAlgorithm = tidesdb::CompressionAlgorithm::Zstd;     // Zstandard
#ifndef __sun
cfConfig.compressionAlgorithm = tidesdb::CompressionAlgorithm::Snappy;   // Snappy (not available on SunOS)
#endif

db.createColumnFamily("my_cf", cfConfig);

Choosing a Compression Algorithm

Workload	Recommended Algorithm	Rationale
General purpose	CompressionAlgorithm::LZ4	Best balance of speed and compression
Write-heavy	CompressionAlgorithm::LZ4Fast	Minimize CPU overhead on writes
Storage-constrained	CompressionAlgorithm::Zstd	Maximum compression ratio
Read-heavy	CompressionAlgorithm::Zstd	Reduce I/O bandwidth, decompression is fast
Pre-compressed data	CompressionAlgorithm::None	Avoid double compression overhead
CPU-constrained	CompressionAlgorithm::None or LZ4Fast	Minimize CPU usage

Compression algorithm can be changed at runtime via updateRuntimeConfig, but the change only affects new SSTables. Existing SSTables retain their original compression and are decompressed correctly during reads. Different column families can use different compression algorithms.

B+tree KLog Format (Optional)

Column families can optionally use a B+tree structure for the key log instead of the default block-based format. The B+tree klog format offers faster point lookups through O(log N) tree traversal rather than linear block scanning.

auto cfConfig = tidesdb::ColumnFamilyConfig::defaultConfig();
cfConfig.useBtree = true;  // Enable B+tree klog format

db.createColumnFamily("btree_cf", cfConfig);

Characteristics

• Point lookups · O(log N) tree traversal with binary search at each node
• Range scans · Doubly-linked leaf nodes enable efficient bidirectional iteration
• Immutable · Tree is bulk-loaded from sorted memtable data during flush
• Compression · Nodes compress independently using the same algorithms (LZ4, LZ4-FAST, Zstd)
• Large values · Values exceeding klogValueThreshold are stored in vlog, same as block-based format
• Bloom filter · Works identically - checked before tree traversal

When to use B+tree klog format

• Read-heavy workloads with frequent point lookups
• Workloads where read latency is more important than write throughput
• Large SSTables where block scanning becomes expensive

Tradeoffs

• Slightly higher write amplification during flush (building tree structure)
• Larger metadata overhead per node compared to block-based format
• Block-based format may be faster for sequential scans of entire SSTables

Important · useBtree cannot be changed after column family creation. Different column families can use different formats.

Error Handling

The C++ wrapper uses exceptions for error handling. All errors throw tidesdb::Exception.

try {
    auto cf = db.getColumnFamily("my_cf");

    auto txn = db.beginTransaction();
    txn.put(cf, "key", "value", -1);
    txn.commit();
} catch (const tidesdb::Exception& e) {
    std::cerr << "Error: " << e.what() << std::endl;
    std::cerr << "Code: " << static_cast<int>(e.code()) << std::endl;
}

Error Codes

• ErrorCode::Success (0) · Operation successful
• ErrorCode::Memory (-1) · Memory allocation failed
• ErrorCode::InvalidArgs (-2) · Invalid arguments
• ErrorCode::NotFound (-3) · Key not found
• ErrorCode::IO (-4) · I/O error
• ErrorCode::Corruption (-5) · Data corruption
• ErrorCode::Exists (-6) · Resource already exists
• ErrorCode::Conflict (-7) · Transaction conflict
• ErrorCode::TooLarge (-8) · Key or value too large
• ErrorCode::MemoryLimit (-9) · Memory limit exceeded
• ErrorCode::InvalidDB (-10) · Invalid database handle
• ErrorCode::Unknown (-11) · Unknown error
• ErrorCode::Locked (-12) · Database is locked

Error categories

• ErrorCode::Corruption indicates data integrity issues requiring immediate attention
• ErrorCode::Conflict indicates transaction conflicts (retry may succeed)
• ErrorCode::Memory, ErrorCode::MemoryLimit, ErrorCode::TooLarge indicate resource constraints
• ErrorCode::NotFound, ErrorCode::Exists are normal operational conditions, not failures

Complete Example

#include <tidesdb/tidesdb.hpp>
#include <iostream>
#include <ctime>

int main() {
    try {
        tidesdb::Config config;
        config.dbPath = "./example_db";
        config.numFlushThreads = 1;
        config.numCompactionThreads = 1;
        config.logLevel = tidesdb::LogLevel::Info;
        config.blockCacheSize = 64 * 1024 * 1024;
        config.maxOpenSSTables = 256;

        tidesdb::TidesDB db(config);

        auto cfConfig = tidesdb::ColumnFamilyConfig::defaultConfig();
        cfConfig.writeBufferSize = 64 * 1024 * 1024;
        cfConfig.compressionAlgorithm = tidesdb::CompressionAlgorithm::LZ4;
        cfConfig.enableBloomFilter = true;
        cfConfig.bloomFPR = 0.01;
        cfConfig.syncMode = tidesdb::SyncMode::Interval;
        cfConfig.syncIntervalUs = 128000;

        db.createColumnFamily("users", cfConfig);

        auto cf = db.getColumnFamily("users");

        {
            auto txn = db.beginTransaction();
            txn.put(cf, "user:1", "Alice", -1);
            txn.put(cf, "user:2", "Bob", -1);

            auto ttl = std::time(nullptr) + 30;
            txn.put(cf, "session:abc", "temp_data", ttl);

            txn.commit();
        }

        {
            auto txn = db.beginTransaction();
            auto value = txn.get(cf, "user:1");
            std::string valueStr(value.begin(), value.end());
            std::cout << "user:1 = " << valueStr << std::endl;
        }

        {
            auto txn = db.beginTransaction();
            auto iter = txn.newIterator(cf);

            std::cout << "\nAll entries:" << std::endl;
            iter.seekToFirst();
            while (iter.valid()) {
                auto key = iter.key();
                auto value = iter.value();
                std::string keyStr(key.begin(), key.end());
                std::string valueStr(value.begin(), value.end());
                std::cout << "  " << keyStr << " = " << valueStr << std::endl;
                iter.next();
            }
        }

        auto stats = cf.getStats();
        std::cout << "\nColumn Family Statistics:" << std::endl;
        std::cout << "  Number of Levels: " << stats.numLevels << std::endl;
        std::cout << "  Memtable Size: " << stats.memtableSize << " bytes" << std::endl;
        std::cout << "  Uses B+tree: " << (stats.useBtree ? "yes" : "no") << std::endl;

        db.dropColumnFamily("users");

    } catch (const tidesdb::Exception& e) {
        std::cerr << "Error: " << e.what() << std::endl;
        return 1;
    }

    return 0;
}

Isolation Levels

TidesDB supports five MVCC isolation levels:

auto txn = db.beginTransaction(tidesdb::IsolationLevel::ReadCommitted);
txn.commit();

Available Isolation Levels

• IsolationLevel::ReadUncommitted · Sees all data including uncommitted changes
• IsolationLevel::ReadCommitted · Sees only committed data (default)
• IsolationLevel::RepeatableRead · Consistent snapshot, phantom reads possible
• IsolationLevel::Snapshot · Write-write conflict detection
• IsolationLevel::Serializable · Full read-write conflict detection (SSI)

Savepoints

Savepoints allow partial rollback within a transaction:

auto txn = db.beginTransaction();

txn.put(cf, "key1", "value1", -1);

txn.savepoint("sp1");
txn.put(cf, "key2", "value2", -1);

txn.rollbackToSavepoint("sp1");

txn.releaseSavepoint("sp1");

txn.commit();

Savepoint API

• savepoint(name) · Create a savepoint
• rollbackToSavepoint(name) · Rollback to savepoint
• releaseSavepoint(name) · Release savepoint without rolling back

Savepoint behavior

• Savepoints capture the transaction state at a specific point
• Rolling back to a savepoint discards all operations after that savepoint
• Releasing a savepoint frees its resources without rolling back
• Multiple savepoints can be created with different names
• Creating a savepoint with an existing name updates that savepoint
• Savepoints are automatically freed when the transaction commits or rolls back

Transaction Reset

Transaction::reset resets a committed or aborted transaction for reuse with a new isolation level. This avoids the overhead of freeing and reallocating transaction resources in hot loops.

auto cf = db.getColumnFamily("my_cf");

auto txn = db.beginTransaction();

txn.put(cf, "key1", "value1", -1);
txn.commit();

txn.reset(tidesdb::IsolationLevel::ReadCommitted);

txn.put(cf, "key2", "value2", -1);
txn.commit();

Behavior

• The transaction must be committed or aborted before reset; resetting an active transaction throws an exception
• Internal buffers are retained to avoid reallocation
• A fresh transaction ID and snapshot sequence are assigned based on the new isolation level
• The isolation level can be changed on each reset (e.g., ReadCommitted to RepeatableRead)

When to use

• Batch processing · Reuse a single transaction across many commit cycles in a loop
• Connection pooling · Reset a transaction for a new request without reallocation
• High-throughput ingestion · Reduce malloc/free overhead in tight write loops

Reset after rollback

auto txn = db.beginTransaction();

txn.put(cf, "key", "value", -1);
txn.rollback();

txn.reset(tidesdb::IsolationLevel::ReadCommitted);
txn.put(cf, "new_key", "new_value", -1);
txn.commit();

Multi-Column-Family Transactions

TidesDB supports atomic transactions across multiple column families with true all-or-nothing semantics.

auto usersCf = db.getColumnFamily("users");
auto ordersCf = db.getColumnFamily("orders");

auto txn = db.beginTransaction();

txn.put(usersCf, "user:1000", "John Doe", -1);

txn.put(ordersCf, "order:5000", "user:1000|product:A", -1);

txn.commit();

Multi-CF guarantees

• Either all CFs commit or none do (atomic)
• Automatically detected when operations span multiple CFs
• Uses global sequence numbers for atomic ordering

Default Configuration

Get default configurations for database and column families.

auto defaultDbConfig = tidesdb::TidesDB::defaultConfig();
defaultDbConfig.dbPath = "./mydb";
tidesdb::TidesDB db(defaultDbConfig);

auto defaultCfConfig = tidesdb::ColumnFamilyConfig::defaultConfig();
db.createColumnFamily("my_cf", defaultCfConfig);

Configuration Persistence

Load and save column family configuration from/to INI files.

auto config = tidesdb::ColumnFamilyConfig::loadFromIni("config.ini", "my_cf");
db.createColumnFamily("my_cf", config);

auto cfConfig = tidesdb::ColumnFamilyConfig::defaultConfig();
cfConfig.writeBufferSize = 128 * 1024 * 1024;
tidesdb::ColumnFamilyConfig::saveToIni("config.ini", "my_cf", cfConfig);

Custom Comparators

Register custom comparators for controlling key sort order.

int myReverseCompare(const uint8_t* key1, size_t key1_size,
                     const uint8_t* key2, size_t key2_size, void* ctx) {
    (void)ctx;
    int result = memcmp(key1, key2, std::min(key1_size, key2_size));
    if (result == 0) {
        return (key1_size < key2_size) ? 1 : (key1_size > key2_size) ? -1 : 0;
    }
    return -result;  // Reverse order
}

db.registerComparator("reverse", myReverseCompare);

auto cfConfig = tidesdb::ColumnFamilyConfig::defaultConfig();
cfConfig.comparatorName = "reverse";
db.createColumnFamily("reverse_cf", cfConfig);

Built-in comparators

• "memcmp" (default) · Binary byte-by-byte comparison
• "lexicographic" · Null-terminated string comparison
• "uint64" · Unsigned 64-bit integer comparison
• "int64" · Signed 64-bit integer comparison
• "reverse" · Reverse binary comparison
• "case_insensitive" · Case-insensitive ASCII comparison

Important

Once a comparator is set for a column family, it cannot be changed without corrupting data.

Retrieving a Registered Comparator

Use getComparator to retrieve a previously registered comparator by name:

tidesdb_comparator_fn fn = nullptr;
void* ctx = nullptr;

db.getComparator("timestamp_desc", &fn, &ctx);
// fn and ctx are now populated

Use cases

• Validation · Check if a comparator is registered before creating a column family
• Debugging · Verify comparator registration during development
• Dynamic configuration · Query available comparators at runtime

Thread Pools

TidesDB uses separate thread pools for flush and compaction operations. Understanding the parallelism model is important for optimal configuration.

Parallelism semantics

• Cross-CF parallelism · Multiple flush/compaction workers CAN process different column families in parallel
• Within-CF serialization · A single column family can only have one flush and one compaction running at any time (enforced by atomic isFlushing and isCompacting flags)
• No intra-CF memtable parallelism · Even if a CF has multiple immutable memtables queued, they are flushed sequentially

Thread pool sizing guidance

• Single column family · Set numFlushThreads = 1 and numCompactionThreads = 1. Additional threads provide no benefit since only one operation per CF can run at a time
• Multiple column families · Set thread counts up to the number of column families for maximum parallelism. With N column families and M workers (where M ≤ N), throughput scales linearly

Configuration

tidesdb::Config config;
config.dbPath = "./mydb";
config.numFlushThreads = 2;                    // Flush thread pool size (default: 2)
config.numCompactionThreads = 2;               // Compaction thread pool size (default: 2)
config.blockCacheSize = 64 * 1024 * 1024;      // 64MB global block cache (default: 64MB)
config.maxOpenSSTables = 256;                   // LRU cache for SSTable objects (default: 256, each has 2 FDs)

tidesdb::TidesDB db(config);

maxOpenSSTables is a storage-engine-level configuration, not a column family configuration. It controls the LRU cache size for SSTable structures. Each SSTable uses 2 file descriptors (klog + vlog), so 256 SSTables = 512 file descriptors.

Testing

cmake -S . -B build -DTIDESDB_CPP_BUILD_TESTS=ON
cmake --build build

cd build
ctest --output-on-failure