Impala DML for Kudu Tables
In DML statements, Impala supports specific features and properties that only apply to Kudu tables.
DELETE statements let
you modify data within Kudu tables without rewriting substantial amounts
of table data.
UPSERT statement acts as a combination of
UPDATE, inserting rows
where the primary key does not already exist, and updating the non-primary
key columns where the primary key does already exist in the table.
INSERT statement for Kudu tables honors the unique
NOT NULL requirements for the primary key columns.
Because Impala and Kudu do not support transactions, the effects of any
DELETE statement are immediately visible. For example,
you cannot do a sequence of
UPDATE statements and only
make the changes visible after all the statements are finished. Also, if a
DML statement fails partway through, any rows that were already inserted,
deleted, or changed remain in the table; there is no rollback mechanism to
undo the changes.
In particular, an
INSERT ... SELECT statement that
refers to the table being inserted into might insert more rows than
expected, because the
SELECT part of the statement sees
some of the new rows being inserted and processes them again.
operations into Kudu tables automatically add an exchange and a sort node
to the plan that partitions and sorts the rows according to the
partitioning/primary key scheme of the target table (unless the number of
rows to be inserted is small enough to trigger single node execution).
Since Kudu partitions and sorts rows on write, pre-partitioning and
sorting takes some of the load off of Kudu and helps large
INSERT operations to complete without timing out.
However, this default behavior may slow down the end-to-end performance of
You can use the
/* +NOCLUSTERED */ and
+NOSHUFFLE */ hints together to disable partitioning and
sorting before the rows are sent to Kudu. Additionally, since sorting may
consume a large amount of memory, consider setting the
MEM_LIMIT query option for those queries.
Loading Data into Kudu Tables
Kudu tables are well-suited to use cases where data arrives
continuously, in small or moderate volumes. To bring data into Kudu
tables, use the Impala
LOAD DATA statement does not support Kudu tables.
Because Kudu manages its own storage layer that is optimized for
smaller block sizes than HDFS, and performs its own maintenance to keep
data evenly distributed, it is not subject to the
files issue and does not need explicit reorganization and
compaction as the data grows over time.
The partitions within a Kudu table can be specified to cover a variety of possible data distributions, instead of hardcoding a new partition for each new day, hour, and so on, which can lead to inefficient, hard-to-scale, and hard-to-manage partition schemes with HDFS tables.
UPSERToperations should produce an identical result even when executed multiple times.
- If a bulk operation is in danger of exceeding capacity limits due to timeouts or high memory usage, split it into a series of smaller operations.
- Avoid running concurrent ETL operations where the end results
depend on precise ordering. In particular, do not rely on an
INSERT ... SELECTstatement that selects from the same table into which it is inserting, unless you include extra conditions in the
WHEREclause to avoid reading the newly inserted rows within the same statement.
- Because relationships between tables cannot be enforced by Impala and Kudu, and cannot be committed or rolled back together, do not expect transactional semantics for multi-table operations.
Consistency Considerations for Kudu Tables
Kudu tables have consistency characteristics such as uniqueness, controlled by the primary key columns and non-nullable columns. The emphasis for consistency is on preventing duplicate or incomplete data from being stored in a table.
Currently, Kudu does not enforce strong consistency for order of operations, total success or total failure of a multi-row statement, or data that is read while a write operation is in progress. Changes are applied atomically to each row, but not applied as a single unit to all rows affected by a multi-row DML statement. That is, Kudu does not currently have atomic multi-row statements or isolation between statements.
If some rows are rejected during a DML operation because of a mismatch
with duplicate primary key values,
constraints, and so on, the statement succeeds with a warning. Impala
still inserts, deletes, or updates the other rows that are not affected
by the constraint violation.
Consequently, the number of rows affected by a DML operation on a Kudu table might be different than you expect.
Because there is no strong consistency guarantee for information being
inserted into, deleted from, or updated across multiple tables
simultaneously, consider denormalizing the data where practical. That
is, if you run separate
INSERT statements to insert
related rows into two different tables, one
might fail while the other succeeds, leaving the data in an inconsistent
state. Even if both inserts succeed, a join query might happen during
the interval between the completion of the first and second statements,
and the query would encounter incomplete inconsistent data.
Denormalizing the data into a single wide table can reduce the
possibility of inconsistency due to multi-table operations.
Information about the number of rows affected by a DML operation is
reported in impala-shell output, and in the
PROFILE output, but is not currently reported to
HiveServer2 clients such as JDBC or ODBC applications.
Handling Date, Time, or Timestamp Data with Kudu
TIMESTAMPcolumns in Kudu tables. The behavior of
TIMESTAMPfor Kudu tables has some special considerations:
Any nanoseconds in the original 96-bit value produced by Impala are not stored, because Kudu represents date/time columns using 64-bit values. The nanosecond portion of the value is rounded, not truncated. Therefore, a
TIMESTAMPvalue that you store in a Kudu table might not be bit-for-bit identical to the value returned by a query.
The conversion between the Impala 96-bit representation and the Kudu 64-bit representation introduces some performance overhead when reading or writing
TIMESTAMPcolumns. You can minimize the overhead during writes by performing inserts through the Kudu API. Because the overhead during reads applies to each query, you might continue to use a
BIGINTcolumn to represent date/time values in performance-critical applications.
TIMESTAMPtype has a narrower range for years than the underlying Kudu data type. Impala can represent years 1400-9999. If year values outside this range are written to a Kudu table by a non-Impala client, Impala returns
NULLby default when reading those
TIMESTAMPvalues during a query. Or, if the
ABORT_ON_ERRORquery option is enabled, the query fails when it encounters a value with an out-of-range year.