About this Document
Apache HBase (TM) is not an ACID compliant database. However, it does guarantee certain specific
This specification enumerates the ACID properties of HBase.
For the sake of common vocabulary, we define the following terms:
- an operation is atomic if it either completes entirely or not at all
all actions cause the table to transition from one valid state directly to another
(eg a row will not disappear during an update, etc)
an operation is isolated if it appears to complete independently of any other concurrent transaction
- any update that reports "successful" to the client will not be lost
- an update is considered visible if any subsequent read will see the update as having been committed
The terms must and may are used as specified by RFC 2119.
In short, the word "must" implies that, if some case exists where the statement
is not true, it is a bug. The word "may" implies that, even if the guarantee
is provided in a current release, users should not rely on it.
APIs to consider
- Read APIs
- Write APIs
- Combination (read-modify-write) APIs
- All mutations are atomic within a row. Any put will either wholely succeed or wholely fail.
- An operation that returns a "success" code has completely succeeded.
- An operation that returns a "failure" code has completely failed.
- An operation that times out may have succeeded and may have failed. However,
it will not have partially succeeded or failed.
- This is true even if the mutation crosses multiple column families within a row.
- APIs that mutate several rows will _not_ be atomic across the multiple rows.
For example, a multiput that operates on rows 'a','b', and 'c' may return having
mutated some but not all of the rows. In such cases, these APIs will return a list
of success codes, each of which may be succeeded, failed, or timed out as described above.
- The checkAndPut API happens atomically like the typical compareAndSet (CAS) operation
found in many hardware architectures.
- The order of mutations is seen to happen in a well-defined order for each row, with no
interleaving. For example, if one writer issues the mutation "a=1,b=1,c=1" and
another writer issues the mutation "a=2,b=2,c=2", the row must either
be "a=1,b=1,c=1" or "a=2,b=2,c=2" and must not be something
- Please note that this is not true _across rows_ for multirow batch mutations.
Consistency and Isolation
- All rows returned via any access API will consist of a complete row that existed at
some point in the table's history.
- This is true across column families - i.e a get of a full row that occurs concurrent
with some mutations 1,2,3,4,5 will return a complete row that existed at some point in time
between mutation i and i+1 for some i between 1 and 5.
- The state of a row will only move forward through the history of edits to it.
Consistency of Scans
A scan is not a consistent view of a table. Scans do
not exhibit snapshot isolation.
Rather, scans have the following properties:
Any row returned by the scan will be a consistent view (i.e. that version
of the complete row existed at some point in time) 
A scan will always reflect a view of the data at least as new as
the beginning of the scan. This satisfies the visibility guarantees
- For example, if client A writes data X and then communicates via a side
channel to client B, any scans started by client B will contain data at least
as new as X.
- A scan _must_ reflect all mutations committed prior to the construction
of the scanner, and _may_ reflect some mutations committed subsequent to the
construction of the scanner.
- Scans must include all data written prior to the scan (except in
the case where data is subsequently mutated, in which case it _may_ reflect
Those familiar with relational databases will recognize this isolation level as "read committed".
Please note that the guarantees listed above regarding scanner consistency
are referring to "transaction commit time", not the "timestamp"
field of each cell. That is to say, a scanner started at time t may see edits
with a timestamp value greater than t, if those edits were committed with a
"forward dated" timestamp before the scanner was constructed.
- When a client receives a "success" response for any mutation, that
mutation is immediately visible to both that client and any client with whom it
later communicates through side channels. 
- A row must never exhibit so-called "time-travel" properties. That
is to say, if a series of mutations moves a row sequentially through a series of
states, any sequence of concurrent reads will return a subsequence of those states.
- For example, if a row's cells are mutated using the "incrementColumnValue"
API, a client must never see the value of any cell decrease.
- This is true regardless of which read API is used to read back the mutation.
- Any version of a cell that has been returned to a read operation is guaranteed to
be durably stored.
- All visible data is also durable data. That is to say, a read will never return
data that has not been made durable on disk
- Any operation that returns a "success" code (eg does not throw an exception)
will be made durable.
- Any operation that returns a "failure" code will not be made durable
(subject to the Atomicity guarantees above)
- All reasonable failure scenarios will not affect any of the guarantees of this document.
All of the above guarantees must be possible within Apache HBase. For users who would like to trade
off some guarantees for performance, HBase may offer several tuning options. For example:
- Visibility may be tuned on a per-read basis to allow stale reads or time travel.
- Durability may be tuned to only flush data to disk on a periodic basis
 A consistent view is not guaranteed intra-row scanning -- i.e. fetching a portion of
a row in one RPC then going back to fetch another portion of the row in a subsequent RPC.
Intra-row scanning happens when you set a limit on how many values to return per Scan#next
 In the context of Apache HBase, "durably on disk" implies an hflush() call on the transaction
log. This does not actually imply an fsync() to magnetic media, but rather just that the data has been
written to the OS cache on all replicas of the log. In the case of a full datacenter power loss, it is
possible that the edits are not truly durable.
 Puts will either wholely succeed or wholely fail, provided that they are actually sent
to the RegionServer. If the writebuffer is used, Puts will not be sent until the writebuffer is filled
or it is explicitly flushed.