This is a brief introduction to Kudu’s transaction and consistency semantics. For an in-depth technical exposition of most of what is mentioned here, and why it is correct, see the technical report .
Kudu’s transactional semantics and architecture are inspired by state-of-the-art systems such as Spanner  and Calvin . Kudu builds upon decades of database research. The core philosophy is to make the lives of developers easier by providing transactions with simple, strong semantics, without sacrificing performance or the ability to tune to different requirements.
Kudu is designed to eventually be fully ACID, however, multi-tablet transactions are not yet implemented. As such, this discussion focuses on single-tablet write operations, and only briefly touches multi-tablet reads. Eventually Kudu will support fully strict-serializable semantics. In fact it already does in a limited context, but not all corner cases are covered as this is still a work in progress.
Kudu currently allows the following operations:
Write operations are sets of rows to be inserted, updated, or deleted in the storage engine, in a single tablet with multiple replicas. Write operations do not have separate "read sets" i.e. they do not scan existing data before performing the write. Each write is only concerned with previous state of the rows that are about to change. Writes are not "committed" explicitly by the user. Instead, they are committed automatically by the system, after completion.
Scans are read operations that can traverse multiple tablets and read information with some consistency or correctness guarantees. Scans can perform time-travel reads, i.e. the user is able to set a scan timestamp in the past and get back results that reflect the state of the storage engine at that point in time.
Before We Begin
Kudu employs Multiversion Concurrency Control (MVCC) and the Raft consensus algorithm . Each write operation in Kudu must go through the tablet’s leader.
The leader acquires all locks for the rows that it will change.
The leader assigns the write a timestamp before the write is submitted for replication. This timestamp will be the write’s "tag" in MVCC.
After a majority of replicas acknowledges the change, the actual rows are changed.
After the changes are complete, they are made visible to concurrent writes and reads, atomically.
All replicas of a tablet observe the same order of operations, and if a write operation is assigned timestamp n and changes row x, a second write operation at timestamp m > n is guaranteed to see the new value of x.
This strict ordering of lock acquisition and timestamp assignment is enforced to be consistent across all replicas of a tablet through consensus. Therefore, write operations are totally ordered with regard to clock-assigned timestamps, relative to other writes in the same tablet. In other words, writes have strict-serializable semantics, though in an admittedly limited context. See this blog post for a little more context regarding what these semantics mean.
While Isolated and Durable in an ACID sense, multi-row write operations are not yet fully Atomic. The failure of a single write in a batch operation does not roll back the operation, but produces per-row errors.
Kudu does not yet support transactions that span multiple tablets. However, consistent snapshot reads are possible (with caveats in the current implementation) as explained below.
Writes from a Kudu client are optionally buffered in memory until they are flushed and sent to the server. When client’s session flushes, the rows for each tablet are batched together, and sent to the tablet server which hosts the leader replica of the tablet. Since there are no inter-tablet transactions, each of these batches represents a single, independent write operation with its own timestamp. However, the client API provides the option to impose some constraints on the assigned timestamps and on how writes to different tablets can be observed by clients.
Kudu, like Spanner, was designed to be externally consistent , preserving consistency even when operations span multiple tablets and even multiple data centers. In practice this means that, if a write operation changes item x at tablet A, and a following write operation changes item y at tablet B, you might want to enforce that if the change to y is observed, the change to x must also be observed. There are many examples where this can be important. For example, if Kudu is storing clickstreams for further analysis, and two clicks follow each other but are stored in different tablets, subsequent clicks should be assigned subsequent timestamps so that the causal relationship between them is captured.
Kudu’s default external consistency mode is called
See  for an extensive explanation on how it works. In brief, this mode causes writes
from a single client to be automatically externally consistent. In the clickstream scenario
above, if the two clicks are submitted by different client instances, the application must
manually propagate timestamps from one client to the other for the causal relationship
to be captured.
Timestamps between clients a and b can be propagated as follows:
AsyncKuduClient#getLastPropagatedTimestamp() on client a,
propagate the timestamp to client b, and call
AsyncKuduClient#setLastPropagatedTimestamp() on client b.
KuduClient::GetLatestObservedTimestamp() on client a,
propagate the timestamp to client b, and call
KuduClient::SetLatestObservedTimestamp() on client b.
Kudu also has an experimental implementation of an external consistency
model used in Google’s Spanner , called
by tightly synchronizing the clocks on all machines in the cluster. Then, when a
write occurs, timestamps are assigned and the results of the write are not made
visible until enough time has passed so that no other machine in the cluster could
possibly assign a lower timestamp to a following write.
When using this mode, the latency of writes is tightly tied to the accuracy of clocks on all the cluster hosts, and using this mode with loose clock synchronization causes writes to take a long time to complete or even time out. See Known Issues and Limitations.
COMMIT_WAIT consistency mode may be selected as follows:
Scans are read operations performed by clients that may span one or more rows across one or more tablets. When a server receives a scan request, it takes a snapshot of the MVCC state and then proceeds in one of two ways depending on the read mode selected by the user. The mode may be selected as follows:
The following modes are available in both clients:
This is the default read mode. The server takes a snapshot of the MVCC state and proceeds with the read immediately. Reads in this mode only yield 'Read Committed' isolation.
In this read mode, scans are consistent and repeatable. A
timestamp for the snapshot is selected either by the server, or set
explicitly by the user through
KuduScanner::SetSnapshotMicros(). Explicitly setting
the timestamp is recommended; see Recommendations. The server waits until this
timestamp is 'safe' (until all write operations that have a lower timestamp have
completed and are visible). This delay, coupled with an external consistency method,
will eventually allow Kudu to have full
strict-serializable semantics for reads
and writes. This is still a work in progress and some anomalies are still possible
(see Known Issues and Limitations). Only scans in this mode can be fault-tolerant.
Selecting between read modes requires balancing the trade-offs and making a choice
that fits your workload. For instance, a reporting application that needs to
scan the entire database might need to perform careful accounting operations, so that
scan may need to be fault-tolerant, but probably doesn’t require a to-the-microsecond
up-to-date view of the database. In that case, you might choose
and select a timestamp that is a few seconds in the past when the scan starts. On
the other hand, a machine learning workload that is not ingesting the whole data
set and is already statistical in nature might not require the scan to be repeatable,
so you might choose
There are several gaps and corner cases that prevent Kudu from being fully strictly-serializable in some situations, at the moment. Below are the details and next, some recommendations.
COMMIT_WAIT is experimental and requires careful tuning of the
time-synchronization protocol, such as NTP (Network Time Protocol). Its use
is discouraged in production environments.
Impala scans are currently performed as
READ_LATEST and have no consistency
AUTO_BACKGROUND_FLUSH mode, or when using "async" flushing mechanisms,
writes applied to a single client session may become reordered due to the
concurrency of flushing the data to the server. This may be particularly
noticeable if a single row is quickly updated with different values in
succession. This phenomenon affects all client API implementations.
Workarounds are described in the API documentation for the respective
implementations in the docs for
If repeatable snapshot reads are a requirement, use
with a timestamp that is slightly in the past (between 2-5 seconds, ideally).
This will circumvent the anomaly described in Reads (Scans). Even when the
anomaly has been addressed, back-dating the timestamp will always make scans
faster, since they are unlikely to block.
If external consistency is a requirement and you decide to use
time-synchronization protocol needs to be tuned carefully. Each transaction will wait
2x the maximum clock error at the time of execution, which is usually in the 100 msec.
to 1 sec. range with the default settings, maybe more. Thus, transactions would take at least
200 msec. to 2 sec. to complete when using the default settings and may even time out.
A local server should be used as a time server. We’ve performed experiments using the default NTP time source available in a Google Compute Engine data center and were able to obtain a reasonable tight max error bound, usually varying between 12-17 milliseconds.
The following parameters should be adjusted in
/etc/ntp.conf to tighten the maximum error:
server my_server.org iburst minpoll 1 maxpoll 8
tinker dispersion 500
tinker allan 0
The above parameters minimize
[[]] David Alves, Todd Lipcon and Vijay Garg. Technical Report: HybridTime - Accessible Global Consistency with High Clock Uncertainty. April, 2014. http://users.ece.utexas.edu/~garg/pdslab/david/hybrid-time-tech-report-01.pdf
[[]] James C. Corbett, Jeffrey Dean, Michael Epstein, Andrew Fikes, Christopher Frost, J. J. Furman, Sanjay Ghemawat, Andrey Gubarev, Christopher Heiser, Peter Hochschild, Wilson Hsieh, Sebastian Kanthak, Eugene Kogan, Hongyi Li, Alexander Lloyd, Sergey Melnik, David Mwaura, David Nagle, Sean Quinlan, Rajesh Rao, Lindsay Rolig, Yasushi Saito, Michal Szymaniak, Christopher Taylor, Ruth Wang, and Dale Woodford. 2012. Spanner: Google’s globally-distributed database. In Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation (OSDI'12). USENIX Association, Berkeley, CA, USA, 251-264.
[[]] Alexander Thomson, Thaddeus Diamond, Shu-Chun Weng, Kun Ren, Philip Shao, and Daniel J. Abadi. 2012. Calvin: fast distributed transactions for partitioned database systems. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data (SIGMOD '12). ACM, New York, NY, USA, 1-12. DOI=10.1145/2213836.2213838 http://doi.acm.org/10.1145/2213836.2213838
[[]] Diego Ongaro and John Ousterhout. 2014. In search of an understandable consensus algorithm. In Proceedings of the 2014 USENIX conference on USENIX Annual Technical Conference (USENIX ATC'14), Garth Gibson and Nickolai Zeldovich (Eds.). USENIX Association, Berkeley, CA, USA, 305-320.
[[]] Kwei-Jay Lin, "Consistency issues in real-time database systems," in System Sciences, 1989. Vol.II: Software Track, Proceedings of the Twenty-Second Annual Hawaii International Conference on , vol.2, no., pp.654-661 vol.2, 3-6 Jan 1989 doi: 10.1109/HICSS.1989.48069