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What the 2017 presentation covered
Apache Tephra’s presentations page lists “Transaction in HBase, Apache Big Data North America 2017.” Indexed slide text titles the session “Transactions in HBase,” names Andreas Neumann and Gokul Gunasekaran, and dates it June 2017. The stated goals were to explain why transactions matter, introduce optimistic concurrency control, and compare Omid, Tephra, and Trafodion.
The historical slides framed the problem around consistency during concurrent workloads, partial outputs after failures, a consistent view for long-running jobs, and near-real-time processing. They described HBase as a distributed key-value store partitioned into regions. These are the presentation’s framing and should be read in that 2017 context.
Does HBase support ACID transactions?
Not as a general, built-in transaction spanning arbitrary rows, regions, tables, or multiple calls. The presentation characterized HBase atomicity as applying at the cell, row, and region-operation level, and not extending across regions, tables, or multiple calls. It also described HBase as lacking a built-in rollback mechanism and noted timestamp filters as providing some isolation. That is the talk’s 2017 summary, not a complete statement of behavior for every current HBase release or integration.
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In practical terms, an application using ordinary HBase operations should not assume that several writes across rows or tables will succeed or fail together. To get broader transaction guarantees, applications can use a transaction layer—but its setup and compatibility depend on the exact HBase version, distribution, and integration.
How optimistic concurrency control works
The talk introduced optimistic concurrency control as a way to let operations proceed without first locking all the data they may touch. A transaction checks for conflicts when it commits; if conflicting work is detected, the transaction is rolled back and retried. The slides contrasted this with locking, which can cause waiting and deadlocks.
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- Run the work: the application reads and writes through the transaction mechanism while other operations may proceed concurrently.
- Check at commit: the mechanism determines whether another operation has conflicted with the transaction.
- Resolve a conflict: conflicting work is rolled back and retried, as described in the presentation.
Optimistic control is not a guarantee that conflicts never occur; it shifts conflict detection to commit time. Whether it is appropriate depends on the workload and the transaction system’s behavior.
Ways to add broader transaction behavior
The presentation names Omid, Tephra, and Trafodion as projects to compare. The available project documentation supports describing some capabilities, but does not establish a current, version-specific ranking or recommendation across all three.
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| Approach | What the cited material establishes | Important qualification |
|---|---|---|
| Ordinary HBase operations | The 2017 presentation describes atomicity at cell, row, and region-operation level, not a general transaction across regions, tables, or multiple calls. | This is the presentation’s 2017 account; verify behavior for the deployed HBase release and integrations. |
| Apache Phoenix transaction integration | Phoenix documentation describes configured cross-row and cross-table ACID support, including a transaction manager and enabling transactional tables. | This is an added, configured capability—not automatically active for ordinary HBase tables. Setup and availability depend on the Phoenix/HBase versions and distribution. |
| Apache Omid | Apache project documentation describes bundling multiple HBase reads and writes into ACID transactions. | Confirm compatibility and operational suitability for the versions in use; the cited material does not provide a current comparative recommendation. |
| Tephra and Trafodion | The presentation names both in its comparison of transaction approaches. | The cited material does not establish their current feature status, version compatibility, or suitability relative to other choices. |
How to choose a transaction approach
Start with the guarantee the application actually needs, then validate the concrete integration rather than assuming that the HBase API alone provides it. Compare options on these dimensions:
- Atomicity scope: does the transaction cover one row, multiple rows, multiple regions, or multiple tables?
- Isolation and conflict detection: how are conflicting reads and writes detected, and when are they surfaced?
- Rollback and recovery: what happens to writes after a failed or conflicting transaction?
- Application changes: does the client use a different API or explicitly mark tables as transactional?
- Services and configuration: is a transaction manager or another service required?
- Compatibility and operations: does the combination of HBase version, Phoenix or other integration, distribution, and deployment support the required behavior?
Before relying on cross-row guarantees, check the documentation for the exact deployed versions and test failure and conflict scenarios in that environment. The 2017 session is useful as a conceptual map of the problem and approaches, not as a current compatibility matrix.
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