Lindorm Tunnel Service (LTS) is a data ecosystem service customized for the business scenarios of ApsaraDB for HBase. It handles data migration, synchronization, subscription, and archiving — letting you move data between clusters, stream real-time updates, dump data to a data lake, and write back to a data warehouse, all through a single service. LTS was formerly known as Big DataHub Service (BDS).
Core capabilities
Cloud-native distributed system: LTS runs as a distributed system on Elastic Compute Service (ECS). Scale horizontally by adding nodes to match your throughput requirements.
Ease of use: Set up migration, import, subscription, and archiving tasks in a few clicks. For a migration task, specify the source, destination, and columns to sync — LTS replicates schemas, full data, and incremental data automatically.
High security and reliability: Before a task starts, LTS checks network connectivity and security. While the task runs, it monitors synchronization latency and destination cluster storage in real time, applies traffic throttling, and sends alerts. After the task completes, LTS verifies the synchronized data.
Cost-efficiency: LTS replicates data based on physical files rather than API calls, which is 10 times more efficient than traditional data replication. Built on optimized Apache HBase, Apache Phoenix, and Apache Cassandra internals, it also provides optimized CPU, cache, memory, and network I/O capabilities.
Features
| Feature | Scenario | References |
|---|---|---|
| Data migration or synchronization between HBase clusters | Seamless data migration between existing clusters and new clusters, cluster upgrades, online and offline workload decoupling, active/standby disaster recovery, and active geo-redundancy | Synchronize full and incremental data |
| Data export from HBase to MaxCompute (previously known as Open Data Processing Service (ODPS)) | Export of historical data and incremental data | Export full data to MaxCompute and Archive incremental data to MaxCompute |
| Subscription to Log Service to synchronize data to HBase | Subscription to real-time data in Log Service to synchronize the data to HBase | Import incremental data from Log Service |
| Subscription to HBase incremental data | Subscription to real-time data of ApsaraDB for HBase Performance-enhanced Edition | Instructions |
Log lifecycle management
When you enable log subscription, LTS retains unconsumed log data for 48 hours. After that period, the subscription is automatically canceled and the retained data is automatically deleted.
Log data may fail to be consumed in two situations: the LTS cluster is released while a task is running, or the synchronization task is suspended.
Log subscription is supported for the following task types: HBase or Lindorm incremental synchronization, data archiving, data backup, and data subscription.
Use cases
Migration without downtime
LTS supports zero-downtime migration for HBase 1.x, HBase 2.x, ApsaraDB for HBase Performance-enhanced Edition, Phoenix 4.x, and Phoenix 5.x.
Migrate historical data and synchronize real-time incremental data in a single task — no service interruption required.
LTS reads data directly from the Hadoop Distributed File System (HDFS) of the source cluster without interacting with the source HBase cluster, minimizing impact on online workloads.
File-based replication cuts traffic by more than 50% compared to API-based replication.
Each node migrates data at up to 100 MB/s. Add nodes horizontally to handle terabyte- or petabyte-scale datasets.
Automatic schema synchronization keeps partitions consistent across source and destination.
Built-in task retry, real-time progress monitoring, and failure alerts keep migrations on track.
Post-migration data verification confirms accuracy.
Online and offline workload decoupling
Synchronize online business data in real time to Object Storage Service (OSS) or HDFS. Use Spark and MapReduce to analyze the data without affecting online query performance.
Active/standby disaster recovery
LTS supports two-way data synchronization between a primary cluster and a secondary cluster. When the primary cluster fails, switch workloads to the secondary cluster. After the primary cluster recovers, use LTS to sync the incremental data from the secondary cluster back to the primary.
Hot/cold data separation for ApsaraDB RDS
When transaction history or other historical data causes performance bottlenecks in ApsaraDB RDS, use LTS to sync RDS data to ApsaraDB for HBase in real time. This separates hot data from cold data. ApsaraDB for HBase provides automatic horizontal scaling, high-concurrency queries, multi-dimensional indexing, and lightweight analysis. Use Streams to subscribe to data updates in order, or sync data from ApsaraDB for HBase to other analytics systems for complex analysis.