ScyllaDB Migration

Migrating a production JanusGraph cluster to ScyllaDB requires strict alignment between storage backend semantics, index synchronization guarantees, and pipeline orchestration. This guide targets graph data engineers, distributed systems developers, and platform teams executing a live ScyllaDB Migration under zero-downtime constraints. The process hinges on precise configuration mapping, deterministic index backfilling, and driver-level connection tuning.

The diagram below outlines the end-to-end migration path from Cassandra to ScyllaDB with index reconciliation before cutover.

flowchart LR
    A["Cassandra cluster"] --> B["Validate CQL compatibility"]
    B --> C["Dual-write / snapshot"]
    C --> D["Bulk load into ScyllaDB"]
    D --> E["Reindex mixed indexes"]
    E --> F["Cutover traffic"]
    F --> G["Verify consistency"]

Protocol Compatibility & Configuration Mapping

The foundation of any successful transition begins with a clear understanding of the JanusGraph Storage Backend Architecture & Configuration. ScyllaDB implements the CQL wire protocol, allowing JanusGraph to reuse its Cassandra-compatible storage adapter with minimal code changes. Protocol compatibility does not guarantee operational parity. You must map legacy topology settings to ScyllaDB’s shard-aware routing model and validate that compaction strategies align with graph traversal access patterns.

Align your janusgraph.properties with ScyllaDB’s default partitioner and network topology before initiating data movement.

properties 
storage.backend=cql
storage.hostname=scylla-node-01,scylla-node-02,scylla-node-03
storage.cql.keyspace=janusgraph_prod
storage.cql.local-datacenter=us-east-1
storage.cql.protocol-version=4
storage.cql.read-consistency-level=LOCAL_QUORUM
storage.cql.write-consistency-level=LOCAL_QUORUM
storage.cql.max-connections-per-host=16
storage.cql.connection-pool-size=8
storage.cql.request-timeout=15000
storage.cql.retry-policy=DowngradingConsistencyRetryPolicy

These settings establish a deterministic baseline. LOCAL_QUORUM prevents cross-datacenter latency spikes while maintaining fault tolerance for graph traversals. The DowngradingConsistencyRetryPolicy ensures transient node failures do not cascade into pipeline stalls. For teams transitioning from legacy Cassandra deployments, reviewing the Cassandra Backend Setup reference clarifies property deltas that impact JanusGraph’s internal transaction manager.

Connection Pooling & Driver Tuning

Connection saturation is the most common failure mode during bulk ingestion or heavy traversal workloads. Proper Connection Pooling requires tuning both the JanusGraph CQL client and the underlying DataStax driver. ScyllaDB’s per-shard architecture benefits from connection-per-core alignment.

Apply the following tuning parameters:

  • Set storage.cql.max-connections-per-host to match your ScyllaDB node vCPU count divided by two.
  • Configure storage.cql.connection-pool-size to handle concurrent traversal threads without queueing.
  • Implement exponential backoff with jitter for transient OverloadedException responses.
  • Monitor connection_pool_active and connection_pool_pending metrics to detect pool exhaustion before traversal latency degrades.

Driver-level timeouts must exceed the worst-case compaction or index flush duration. ScyllaDB’s CQL compatibility documentation outlines protocol-level differences that affect connection lifecycle management.

Index Synchronization & Consistency Models

The critical path in any ScyllaDB Migration is maintaining Apache JanusGraph Storage Backend & Index Synchronization across the transition window. JanusGraph relies on eventual consistency for mixed indexes (Elasticsearch/OpenSearch) and strict consistency for graph indexes. During migration, you must decouple vertex/edge writes from index updates to prevent stale query results.

Graph indexes reside in the storage backend and inherit the configured consistency level. LOCAL_QUORUM guarantees that reads and writes acknowledge a majority of replicas within the local datacenter. Mixed indexes operate asynchronously. The index backend receives mutation events via a transaction log and applies them independently. This architectural split requires explicit backfill sequencing.

For detailed consistency tuning strategies, consult the guide on Optimizing ScyllaDB Read Write Consistency for Graphs. Key synchronization steps include:

  • Disable automatic mixed index updates during the initial bulk load phase.
  • Run ManagementSystem.awaitGraphIndexStatus() to verify graph index readiness.
  • Trigger a full mixed index reindex operation after storage backend stabilization.
  • Validate index freshness using index.search.backend health endpoints before routing production traffic.

Python Pipeline Implementation

Production pipelines require deterministic retry logic to handle coordinator handoffs, temporary node unavailability, and ScyllaDB overload signals. The following example demonstrates a resilient traversal execution pattern using gremlinpython and tenacity.

python 
import time
import logging
from gremlin_python.driver.driver_remote_connection import DriverRemoteConnection
from gremlin_python.process.anonymous_traversal import traversal
from gremlin_python.process.traversal import T
from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
from tenacity.retry import retry_if_exception

logger = logging.getLogger(__name__)

class GraphPipelineError(Exception):
    pass

def is_transient_error(exc: Exception) -> bool:
    """Identify recoverable ScyllaDB/JanusGraph errors."""
    msg = str(exc).lower()
    return any(k in msg for k in ["overloaded", "unavailable", "timeout", "connection reset"])

@retry(
    retry=retry_if_exception_type(Exception) & retry_if_exception(is_transient_error),
    stop=stop_after_attempt(5),
    wait=wait_exponential(multiplier=1, min=2, max=16),
    reraise=True
)
def execute_traversal_with_retry(connection_url: str, traversal_func):
    """Execute a Gremlin traversal with exponential backoff and jitter."""
    try:
        conn = DriverRemoteConnection(connection_url, "g")
        g = traversal().withRemote(conn)
        result = traversal_func(g).toList()
        conn.close()
        return result
    except Exception as e:
        logger.warning("Transient traversal failure: %s. Retrying...", e)
        raise GraphPipelineError(f"Traversal failed after retries: {e}") from e

# Usage example
def fetch_user_neighbors(g):
    return g.V().has("user", "id", "u_123").out("follows").values("name")

try:
    neighbors = execute_traversal_with_retry(
        "ws://janusgraph-gateway:8182/gremlin",
        fetch_user_neighbors
    )
    logger.info("Retrieved %d neighbors successfully.", len(neighbors))
except GraphPipelineError as e:
    logger.error("Pipeline halted: %s", e)
    # Implement circuit breaker or dead-letter queue fallback here

The retry decorator applies exponential backoff with jitter, preventing thundering herd scenarios during cluster rebalancing. For advanced driver-level retry policies, reference the official DataStax Python Driver retry documentation.

Validation & Cutover Procedure

Execute the following validation sequence before shifting production traffic:

  1. Data Parity Check: Run JanusGraphManagement.verify() to confirm schema and index alignment between legacy and Scylla backends.
  2. Index Freshness Audit: Query Elasticsearch/OpenSearch _stats endpoints and compare document counts against JanusGraphManagement.getGraphIndex().
  3. Latency Baseline: Execute standardized traversal workloads (g.V().hasLabel('vertex').limit(1000).elementMap()) and record P95/P99 response times.
  4. Traffic Shift: Route 10% of read traffic to the Scylla-backed JanusGraph instance. Monitor error rates and Overloaded metrics for 15 minutes.
  5. Full Cutover: Update DNS or load balancer routing to direct 100% of traffic. Maintain legacy cluster in standby for 24 hours.
  6. Rollback Trigger: If P99 latency exceeds baseline by >20% or mixed index staleness exceeds 60 seconds, revert routing immediately and investigate compaction backlog.

Zero-downtime migrations succeed through deterministic configuration, explicit consistency boundaries, and automated retry orchestration. Adhere to the outlined parameters and validation gates to ensure stable graph operations post-migration.