Phoenix connector#
The Phoenix connector allows querying data stored in Apache HBase using Apache Phoenix.
Requirements#
To query HBase data through Phoenix, you need:
Network access from the Trino coordinator and workers to the ZooKeeper servers. The default port is 2181.
A compatible version of Phoenix: all 5.x versions starting from 5.1.0 are supported.
Configuration#
To configure the Phoenix connector, create a catalog properties file
etc/catalog/example.properties
with the following contents,
replacing host1,host2,host3
with a comma-separated list of the ZooKeeper
nodes used for discovery of the HBase cluster:
connector.name=phoenix5
phoenix.connection-url=jdbc:phoenix:host1,host2,host3:2181:/hbase
phoenix.config.resources=/path/to/hbase-site.xml
The optional paths to Hadoop resource files, such as hbase-site.xml
are used
to load custom Phoenix client connection properties.
The following Phoenix-specific configuration properties are available:
Property name |
Required |
Description |
---|---|---|
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Yes |
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No |
Comma-separated list of configuration files (e.g. |
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No |
Maximum number of HBase scans that will be performed in a single split. Default is 20. Lower values will lead to more splits in Trino. Can also be set via session propery |
General configuration properties#
The following table describes general catalog configuration properties for the connector:
Property name |
Description |
Default value |
---|---|---|
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Support case insensitive schema and table names. |
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This value should be a duration. |
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Path to a name mapping configuration file in JSON format that allows Trino to disambiguate between schemas and tables with similar names in different cases. |
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Frequency with which Trino checks the name matching configuration file for changes. This value should be a duration. |
(refresh disabled) |
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The duration for which metadata, including table and column statistics, is cached. |
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Cache the fact that metadata, including table and column statistics, is not available |
|
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Maximum number of objects stored in the metadata cache |
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Maximum number of statements in a batched execution. Do not change this setting from the default. Non-default values may negatively impact performance. |
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Push down dynamic filters into JDBC queries |
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Maximum duration for which Trino will wait for dynamic filters to be collected from the build side of joins before starting a JDBC query. Using a large timeout can potentially result in more detailed dynamic filters. However, it can also increase latency for some queries. |
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Appending query metadata#
The optional parameter query.comment-format
allows you to configure a SQL
comment that is sent to the datasource with each query. The format of this
comment can contain any characters and the following metadata:
$QUERY_ID
: The identifier of the query.$USER
: The name of the user who submits the query to Trino.$SOURCE
: The identifier of the client tool used to submit the query, for exampletrino-cli
.$TRACE_TOKEN
: The trace token configured with the client tool.
The comment can provide more context about the query. This additional
information is available in the logs of the datasource. To include environment
variables from the Trino cluster with the comment , use the
${ENV:VARIABLE-NAME}
syntax.
The following example sets a simple comment that identifies each query sent by Trino:
query.comment-format=Query sent by Trino.
With this configuration, a query such as SELECT * FROM example_table;
is
sent to the datasource with the comment appended:
SELECT * FROM example_table; /*Query sent by Trino.*/
The following example improves on the preceding example by using metadata:
query.comment-format=Query $QUERY_ID sent by user $USER from Trino.
If Jane
sent the query with the query identifier
20230622_180528_00000_bkizg
, the following comment string is sent to the
datasource:
SELECT * FROM example_table; /*Query 20230622_180528_00000_bkizg sent by user Jane from Trino.*/
Note
Certain JDBC driver settings and logging configurations might cause the comment to be removed.
Domain compaction threshold#
Pushing down a large list of predicates to the data source can compromise
performance. Trino compacts large predicates into a simpler range predicate
by default to ensure a balance between performance and predicate pushdown.
If necessary, the threshold for this compaction can be increased to improve
performance when the data source is capable of taking advantage of large
predicates. Increasing this threshold may improve pushdown of large
dynamic filters.
The domain-compaction-threshold
catalog configuration property or the
domain_compaction_threshold
catalog session property can be used to adjust the default value of
5000
for this threshold.
Procedures#
system.flush_metadata_cache()
Flush JDBC metadata caches. For example, the following system call flushes the metadata caches for all schemas in the
example
catalogUSE example.example_schema; CALL system.flush_metadata_cache();
Case insensitive matching#
When case-insensitive-name-matching
is set to true
, Trino
is able to query non-lowercase schemas and tables by maintaining a mapping of
the lowercase name to the actual name in the remote system. However, if two
schemas and/or tables have names that differ only in case (such as “customers”
and “Customers”) then Trino fails to query them due to ambiguity.
In these cases, use the case-insensitive-name-matching.config-file
catalog
configuration property to specify a configuration file that maps these remote
schemas/tables to their respective Trino schemas/tables:
{
"schemas": [
{
"remoteSchema": "CaseSensitiveName",
"mapping": "case_insensitive_1"
},
{
"remoteSchema": "cASEsENSITIVEnAME",
"mapping": "case_insensitive_2"
}],
"tables": [
{
"remoteSchema": "CaseSensitiveName",
"remoteTable": "tablex",
"mapping": "table_1"
},
{
"remoteSchema": "CaseSensitiveName",
"remoteTable": "TABLEX",
"mapping": "table_2"
}]
}
Queries against one of the tables or schemes defined in the mapping
attributes are run against the corresponding remote entity. For example, a query
against tables in the case_insensitive_1
schema is forwarded to the
CaseSensitiveName schema and a query against case_insensitive_2
is forwarded
to the cASEsENSITIVEnAME
schema.
At the table mapping level, a query on case_insensitive_1.table_1
as
configured above is forwarded to CaseSensitiveName.tablex
, and a query on
case_insensitive_1.table_2
is forwarded to CaseSensitiveName.TABLEX
.
By default, when a change is made to the mapping configuration file, Trino must
be restarted to load the changes. Optionally, you can set the
case-insensitive-name-mapping.refresh-period
to have Trino refresh the
properties without requiring a restart:
case-insensitive-name-mapping.refresh-period=30s
Non-transactional INSERT#
The connector supports adding rows using INSERT statements.
By default, data insertion is performed by writing data to a temporary table.
You can skip this step to improve performance and write directly to the target
table. Set the insert.non-transactional-insert.enabled
catalog property
or the corresponding non_transactional_insert
catalog session property to
true
.
Note that with this property enabled, data can be corrupted in rare cases where exceptions occur during the insert operation. With transactions disabled, no rollback can be performed.
Querying Phoenix tables#
The default empty schema in Phoenix maps to a schema named default
in Trino.
You can see the available Phoenix schemas by running SHOW SCHEMAS
:
SHOW SCHEMAS FROM example;
If you have a Phoenix schema named web
, you can view the tables
in this schema by running SHOW TABLES
:
SHOW TABLES FROM example.web;
You can see a list of the columns in the clicks
table in the web
schema
using either of the following:
DESCRIBE example.web.clicks;
SHOW COLUMNS FROM example.web.clicks;
Finally, you can access the clicks
table in the web
schema:
SELECT * FROM example.web.clicks;
If you used a different name for your catalog properties file, use
that catalog name instead of example
in the above examples.
Type mapping#
Because Trino and Phoenix each support types that the other does not, this connector modifies some types when reading or writing data. Data types may not map the same way in both directions between Trino and the data source. Refer to the following sections for type mapping in each direction.
Phoenix type to Trino type mapping#
The connector maps Phoenix types to the corresponding Trino types following this table:
Phoenix database type |
Trino type |
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No other types are supported.
Trino type to Phoenix type mapping#
The Phoenix fixed length BINARY
data type is mapped to the Trino variable
length VARBINARY
data type. There is no way to create a Phoenix table in
Trino that uses the BINARY
data type, as Trino does not have an equivalent
type.
The connector maps Trino types to the corresponding Phoenix types following this table:
Trino database type |
Phoenix type |
---|---|
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No other types are supported.
Decimal type handling#
DECIMAL
types with unspecified precision or scale are mapped to a Trino
DECIMAL
with a default precision of 38 and default scale of 0. The scale can
be changed by setting the decimal-mapping
configuration property or the
decimal_mapping
session property to allow_overflow
. The scale of the
resulting type is controlled via the decimal-default-scale
configuration
property or the decimal-rounding-mode
session property. The precision is
always 38.
By default, values that require rounding or truncation to fit will cause a
failure at runtime. This behavior is controlled via the
decimal-rounding-mode
configuration property or the
decimal_rounding_mode
session property, which can be set to UNNECESSARY
(the default), UP
, DOWN
, CEILING
, FLOOR
, HALF_UP
,
HALF_DOWN
, or HALF_EVEN
(see RoundingMode).
Type mapping configuration properties#
The following properties can be used to configure how data types from the connected data source are mapped to Trino data types and how the metadata is cached in Trino.
Property name |
Description |
Default value |
---|---|---|
|
Configure how unsupported column data types are handled:
The respective catalog session property is |
|
|
Allow forced mapping of comma separated lists of data types to convert to
unbounded |
Table properties - Phoenix#
Table property usage example:
CREATE TABLE example_schema.scientists (
recordkey VARCHAR,
birthday DATE,
name VARCHAR,
age BIGINT
)
WITH (
rowkeys = 'recordkey,birthday',
salt_buckets = 10
);
The following are supported Phoenix table properties from https://phoenix.apache.org/language/index.html#options
Property name |
Default value |
Description |
---|---|---|
|
|
Comma-separated list of primary key columns. See further description below |
|
(none) |
List of keys to presplit the table on. See Split Point. |
|
(none) |
Number of salt buckets for this table. |
|
false |
Whether to disable WAL writes in HBase for this table. |
|
false |
Declares whether this table has rows which are write-once, append-only. |
|
|
Default column family name to use for this table. |
rowkeys
#
This is a comma-separated list of columns to be used as the table’s primary key. If not specified, a BIGINT
primary key column named ROWKEY
is generated
, as well as a sequence with the same name as the table suffixed with _seq
(i.e. <schema>.<table>_seq
)
, which is used to automatically populate the ROWKEY
for each row during insertion.
Table properties - HBase#
The following are the supported HBase table properties that are passed through by Phoenix during table creation.
Use them in the same way as above: in the WITH
clause of the CREATE TABLE
statement.
Property name |
Default value |
Description |
---|---|---|
|
|
The maximum number of versions of each cell to keep. |
|
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The minimum number of cell versions to keep. |
|
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Compression algorithm to use. Valid values are |
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Block encoding algorithm to use. Valid values are: |
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Time To Live for each cell. |
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Bloomfilter to use. Valid values are |
SQL support#
The connector provides read and write access to data and metadata in Phoenix. In addition to the globally available and read operation statements, the connector supports the following features:
SQL DELETE#
If a WHERE
clause is specified, the DELETE
operation only works if the
predicate in the clause can be fully pushed down to the data source.