Impala

https://impala.apache.org

The Impala backend is in maintenance-only mode

Feature requests are unlikely to be accepted for the Impala backend, due to the maintenance burden of the components involved.

Install

Install Ibis and dependencies for the Impala backend:

Install with the impala extra:

pip install 'ibis-framework[impala]'

And connect:

import ibis

con = ibis.impala.connect()

1: Adjust connection parameters as needed.

Install for Impala:

conda install -c conda-forge ibis-impala

And connect:

import ibis

con = ibis.impala.connect()

1: Adjust connection parameters as needed.

Install for Impala:

mamba install -c conda-forge ibis-impala

And connect:

import ibis

con = ibis.impala.connect()

1: Adjust connection parameters as needed.

Database methods

create_database

create_database(['self', 'name', 'path=None', 'force=False'])

Create a new Impala database.

Parameters

Name	Description	Default
name	Database name	required
path	Path where to store the database data; otherwise uses the Impala default	`None`
force	Forcibly create the database	`False`

drop_database

drop_database(['self', 'name', 'force=False'])

Drop an Impala database.

Parameters

Name	Type	Description	Default
name		Database name	required
force		If False and there are any tables in this database, raises an IntegrityError	`False`

list_databases

list_databases(['self', 'like=None'])

Table methods

The Backend object itself has many helper utility methods. You’ll find the most methods on ImpalaTable.

table

table(['self', 'name', 'database=None', '**kwargs'])

Construct a table expression.

Parameters

Name	Type	Description	Default
name	str	Table name	required
database	tuple[str, str] \| str \| None	Database name	`None`

Returns

Name	Type	Description
	Table	Table expression

sql

sql(['self', 'query', 'schema=None', 'dialect=None'])

raw_sql

raw_sql(['self', 'query'])

list_tables

list_tables(['self', 'like=None', 'database=None'])

Return the list of table names in the current database.

Parameters

Name	Type	Description	Default
like		A pattern in Python’s regex format.	`None`
database		The database from which to list tables. If not provided, the current database is used.	`None`

Returns

Name	Type	Description
	list[str]	The list of the table names that match the pattern `like`.

drop_table

drop_table(['self', 'name', '*', 'database=None', 'force=False'])

Drop an Impala table.

Parameters

Name	Type	Description	Default
name	str	Table name	required
database	str \| None	Database name	`None`
force	bool	Database may throw exception if table does not exist	`False`

Examples

>>> table = "my_table"
>>> db = "operations"
>>> con.drop_table(table, database=db, force=True)  # quartodoc: +SKIP

insert

insert(['self', 'table_name', 'obj=None', 'database=None', 'overwrite=False', 'partition=None', 'values=None', 'validate=True'])

Insert data into an existing table.

See ImpalaTable.insert for parameters.

Examples

>>> table = "my_table"
>>> con.insert(table, table_expr)  # quartodoc: +SKIP

Completely overwrite contents

>>> con.insert(table, table_expr, overwrite=True)  # quartodoc: +SKIP

truncate_table

truncate_table(['self', 'name', 'database=None'])

Delete all rows from an existing table.

Parameters

Name	Type	Description	Default
name	str	Table name	required
database	str \| None	Database name	`None`

get_schema

get_schema(['self', 'table_name', '*', 'catalog=None', 'database=None'])

Return a Schema object for the indicated table and database.

Parameters

Name	Type	Description	Default
table_name	str	Table name	required
catalog	str \| None	Catalog name. Unused in the impala backend.	`None`
database	str \| None	Database name	`None`

Returns

Name	Type	Description
	Schema	Ibis schema

cache_table

cache_table(['self', 'table_name', '*', 'database=None', "pool='default'"])

Caches a table in cluster memory in the given pool.

Parameters

Name	Description	Default
table_name	Table name	required
database	Database name	`None`
pool	The name of the pool in which to cache the table	`'default'`

Examples

>>> table = "my_table"
>>> db = "operations"
>>> pool = "op_4GB_pool"
>>> con.cache_table("my_table", database=db, pool=pool)  # quartodoc: +SKIP

The best way to interact with a single table is through the ImpalaTable object you get back from Backend.table.

drop

drop(['self'])

Drop the table from the database.

insert

insert(['self', 'obj=None', 'overwrite=False', 'partition=None', 'values=None', 'validate=True'])

Insert into an Impala table.

Parameters

Name	Description	Default
obj	Table expression or DataFrame	`None`
overwrite	If True, will replace existing contents of table	`False`
partition	For partitioned tables, indicate the partition that’s being inserted into, either with an ordered list of partition keys or a dict of partition field name to value. For example for the partition (year=2007, month=7), this can be either (2007, 7) or {‘year’: 2007, ‘month’: 7}.	`None`
values	Unsupported and unused	`None`
validate	If True, do more rigorous validation that schema of table being inserted is compatible with the existing table	`True`

Examples

Append to an existing table

>>> t.insert(table_expr)  # quartodoc: +SKIP

Completely overwrite contents

>>> t.insert(table_expr, overwrite=True)  # quartodoc: +SKIP

describe_formatted

Creating views

drop_table_or_view

drop_table_or_view(['self', 'name', '*', 'database=None', 'force=False'])

Drop view or table.

create_view

create_view(['self', 'name', 'obj', '*', 'database=None', 'overwrite=False'])

Create a new view from an expression.

Parameters

Name	Type	Description	Default
name	str	Name of the new view.	required
obj	ir.Table	An Ibis table expression that will be used to create the view.	required
database	str \| None	Name of the database where the view will be created, if not provided the database’s default is used.	`None`
overwrite	bool	Whether to clobber an existing view with the same name	`False`

Returns

Name	Type	Description
	Table	The view that was created.

Accessing data

delimited_file

delimited_file(['self', 'directory', 'schema', 'name=None', 'database=None', "delimiter=','", 'na_rep=None', 'escapechar=None', 'lineterminator=None', 'external=True'])

Interpret delimited text files as an Ibis table expression.

See the parquet_file method for more details on what happens under the hood.

Parameters

Name	Description	Default
directory	Server directory containing delimited text files	required
schema	Ibis schema	required
name	Name for the table; otherwise random names are generated	`None`
database	Database to create the table in	`None`
delimiter	Character used to delimit columns	`','`
na_rep	Character used to represent NULL values	`None`
escapechar	Character used to escape special characters	`None`
lineterminator	Character used to delimit lines	`None`
external	Create table as EXTERNAL (data will not be deleted on drop).	`True`

Returns

Name	Type	Description
	ImpalaTable	Impala table expression

parquet_file

parquet_file(['self', 'directory', 'schema=None', 'name=None', 'database=None', 'external=True', 'like_file=None', 'like_table=None'])

Create an Ibis table from the passed directory of Parquet files.

The table can be optionally named, otherwise a unique name will be generated.

Parameters

Name	Type	Description	Default
directory	str \| Path	Path	required
schema	sch.Schema \| None	If no schema provided, and neither of the like_* argument is passed, one will be inferred from one of the parquet files in the directory.	`None`
like_file	str \| Path \| None	Absolute path to Parquet file on the server to use for schema definitions. An alternative to having to supply an explicit schema	`None`
like_table	str \| None	Fully scoped and escaped string to an Impala table whose schema we will use for the newly created table.	`None`
name	str \| None	Random unique name generated otherwise	`None`
database	str \| None	Database to create the (possibly temporary) table in	`None`
external	bool	If a table is external, the referenced data will not be deleted when the table is dropped in Impala. Otherwise Impala takes ownership of the Parquet file.	`True`

Returns

Name	Type	Description
	ImpalaTable	Impala table expression

avro_file

avro_file(['self', 'directory', 'avro_schema', 'name=None', 'database=None', 'external=True'])

Create a table to read a collection of Avro data.

Parameters

Name	Description	Default
directory	Server path to directory containing avro files	required
avro_schema	The Avro schema for the data as a Python dict	required
name	Table name	`None`
database	Database name	`None`
external	Whether the table is external	`True`

Returns

Name	Type	Description
	ImpalaTable	Impala table expression

The Impala client object

To use Ibis with Impala, you first must connect to a cluster using the ibis.impala.connect function:

import ibis

client = ibis.impala.connect(host=impala_host, port=impala_port)

By default binary transport mode is used, however it is also possible to use HTTP. Depending on your configuration, additional connection arguments may need to be provided. For the full list of possible connection arguments please refer to the impyla documentation.

import ibis

client = ibis.impala.connect(
    host=impala_host,
    port=impala_port,
    username=username,
    password=password,
    use_ssl=True,
    auth_mechanism='LDAP',
    use_http_transport=True,
    http_path='cliservice',
)

All examples here use the following block of code to connect to impala using docker:

import ibis

client = ibis.impala.connect(host=host)

You can accomplish many tasks directly through the client object, but we additionally provide APIs to streamline tasks involving a single Impala table or database.

Table objects

table

table(['self', 'name', 'database=None'])

Construct a table expression.

Parameters

Name	Type	Description	Default
name	str	Table name	required
database	tuple[str, str] \| str \| None	Database name	`None`

Returns

Name	Type	Description
	Table	Table expression

The client’s table method allows you to create an Ibis table expression referencing a physical Impala table:

table = client.table('functional_alltypes', database='ibis_testing')

ImpalaTable is a Python subclass of the more general Ibis Table that has additional Impala-specific methods. So you can use it interchangeably with any code expecting a Table.

While the client has a drop_table method you can use to drop tables, the table itself has a method drop that you can use:

table.drop()

Expression execution

Ibis expressions have execution methods like to_pandas that compile and run the expressions on Impala or whichever backend is being referenced.

For example:

>>> fa = db.functional_alltypes
>>> expr = fa.double_col.sum()
>>> expr.to_pandas()
331785.00000000006

For longer-running queries, Ibis will attempt to cancel the query in progress if an interrupt is received.

Creating tables

There are several ways to create new Impala tables:

From an Ibis table expression
Empty, from a declared schema
Empty and partitioned

In all cases, you should use the create_table method either on the top-level client connection or a database object.

create_table

create_table(['self', 'name', 'obj=None', '*', 'schema=None', 'database=None', 'temp=None', 'overwrite=False', 'external=False', "format='parquet'", 'location=None', 'partition=None', 'tbl_properties=None', 'like_parquet=None'])

Create a new table using an Ibis table expression or in-memory data.

Parameters

Name	Type	Description	Default
name	str	Table name	required
obj	ir.Table \| pd.DataFrame \| pa.Table \| pl.DataFrame \| pl.LazyFrame \| None	If passed, creates table from select statement results	`None`
schema	sch.SchemaLike \| None	Mutually exclusive with obj, creates an empty table with a particular schema	`None`
database		Database name	`None`
temp	bool \| None	Whether a table is temporary	`None`
overwrite	bool	Do not create table if table with indicated name already exists	`False`
external	bool	Create an external table; Impala will not delete the underlying data when the table is dropped	`False`
format		File format	`'parquet'`
location		Specify the directory location where Impala reads and writes files for the table	`None`
partition		Must pass a schema to use this. Cannot partition from an expression.	`None`
tbl_properties	Mapping[str, Any] \| None	Table properties to set on table creation.	`None`
like_parquet		Can specify instead of a schema	`None`

Creating tables from a table expression

If you pass an Ibis expression to create_table, Ibis issues a CREATE TABLE ... AS SELECT (CTAS) statement:

>>> table = db.table('functional_alltypes')
>>> expr = table.group_by('string_col').size()
>>> db.create_table('string_freqs', expr, format='parquet')

>>> freqs = db.table('string_freqs')
>>> freqs.to_pandas()
  string_col  count
0          9    730
1          3    730
2          6    730
3          4    730
4          1    730
5          8    730
6          2    730
7          7    730
8          5    730
9          0    730

>>> files = freqs.files()
>>> files
                                                Path  Size Partition
0  hdfs://impala:8020/user/hive/warehouse/ibis_te...  584B

>>> freqs.drop()

You can also choose to create an empty table and use insert (see below).

Creating an empty table

To create an empty table, you must declare an Ibis schema that will be translated to the appropriate Impala schema and data types.

As Ibis types are simplified compared with Impala types, this may expand in the future to include a more fine-grained schema declaration.

You can use the create_table method either on a database or client object.

schema = ibis.schema(dict(foo='string', year='int32', month='int16'))
name = 'new_table'
db.create_table(name, schema=schema)

By default, this stores the data files in the database default location. You can force a particular path with the location option.

from getpass import getuser
schema = ibis.schema(dict(foo='string', year='int32', month='int16'))
name = 'new_table'
location = '/home/{}/new-table-data'.format(getuser())
db.create_table(name, schema=schema, location=location)

If the schema matches a known table schema, you can always use the schema method to get a schema object:

>>> t = db.table('functional_alltypes')
>>> t.schema()
ibis.Schema {
  id               int32
  bool_col         boolean
  tinyint_col      int8
  smallint_col     int16
  int_col          int32
  bigint_col       int64
  float_col        float32
  double_col       float64
  date_string_col  string
  string_col       string
  timestamp_col    timestamp
  year             int32
  month            int32
}

Creating a partitioned table

To create an empty partitioned table, include a list of columns to be used as the partition keys.

schema = ibis.schema(dict(foo='string', year='int32', month='int16'))
name = 'new_table'
db.create_table(name, schema=schema, partition=['year', 'month'])

Partitioned tables

Ibis enables you to manage partitioned tables in various ways. Since each partition behaves as its own "subtable" sharing a common schema, each partition can have its own file format, directory path, serialization properties, and so forth.

There are a handful of table methods for adding and removing partitions and getting information about the partition schema and any existing partition data:

add_partition

add_partition(['self', 'spec', 'location=None'])

Add a new table partition.

Partition parameters can be set in a single DDL statement or you can use alter_partition to set them after the fact.

drop_partition

drop_partition(['self', 'spec'])

Drop an existing table partition.

is_partitioned

True if the table is partitioned.

partition_schema

partition_schema(['self'])

Return the schema for the partition columns.

partitions

partitions(['self'])

Return information about the table’s partitions.

Raises an exception if the table is not partitioned.

To address a specific partition in any method that is partition specific, you can either use a dict with the partition key names and values, or pass a list of the partition values:

schema = ibis.schema(dict(foo='string', year='int32', month='int16'))
name = 'new_table'
db.create_table(name, schema=schema, partition=['year', 'month'])

table = db.table(name)

table.add_partition({'year': 2007, 'month', 4})
table.add_partition([2007, 5])
table.add_partition([2007, 6])

table.drop_partition([2007, 6])

We’ll cover partition metadata management and data loading below.

Inserting data into tables

If the schemas are compatible, you can insert into a table directly from an Ibis table expression:

>>> t = db.functional_alltypes
>>> db.create_table('insert_test', schema=t.schema())
>>> target = db.table('insert_test')

>>> target.insert(t[:3])
>>> target.insert(t[:3])
>>> target.insert(t[:3])

>>> target.to_pandas()
     id  bool_col  tinyint_col  ...           timestamp_col  year  month
0  5770      True            0  ... 2010-08-01 00:00:00.000  2010      8
1  5771     False            1  ... 2010-08-01 00:01:00.000  2010      8
2  5772      True            2  ... 2010-08-01 00:02:00.100  2010      8
3  5770      True            0  ... 2010-08-01 00:00:00.000  2010      8
4  5771     False            1  ... 2010-08-01 00:01:00.000  2010      8
5  5772      True            2  ... 2010-08-01 00:02:00.100  2010      8
6  5770      True            0  ... 2010-08-01 00:00:00.000  2010      8
7  5771     False            1  ... 2010-08-01 00:01:00.000  2010      8
8  5772      True            2  ... 2010-08-01 00:02:00.100  2010      8

[9 rows x 13 columns]

>>> target.drop()

If the table is partitioned, you must indicate the partition you are inserting into:

part = {'year': 2007, 'month': 4}
table.insert(expr, partition=part)

Managing table metadata

Ibis has functions that wrap many of the DDL commands for Impala table metadata.

Detailed table metadata: `DESCRIBE FORMATTED`

To get a handy wrangled version of DESCRIBE FORMATTED use the metadata method.

metadata

metadata(['self'])

Return results of DESCRIBE FORMATTED statement.

>>> t = client.table('ibis_testing.functional_alltypes')
>>> meta = t.metadata()
>>> meta
<class 'ibis.backends.impala.metadata.TableMetadata'>
{'info': {'CreateTime': datetime.datetime(2021, 1, 14, 21, 23, 8),
          'Database': 'ibis_testing',
          'LastAccessTime': 'UNKNOWN',
          'Location': 'hdfs://impala:8020/__ibis/ibis-testing-data/parquet/functional_alltypes',
          'Owner': 'root',
          'Protect Mode': 'None',
          'Retention': 0,
          'Table Parameters': {'COLUMN_STATS_ACCURATE': False,
                               'EXTERNAL': True,
                               'STATS_GENERATED_VIA_STATS_TASK': True,
                               'numFiles': 3,
                               'numRows': 7300,
                               'rawDataSize': '-1',
                               'totalSize': 106278,
                               'transient_lastDdlTime': datetime.datetime(2021, 1, 14, 21, 23, 17)},
          'Table Type': 'EXTERNAL_TABLE'},
 'schema': [('id', 'int'),
            ('bool_col', 'boolean'),
            ('tinyint_col', 'tinyint'),
            ('smallint_col', 'smallint'),
            ('int_col', 'int'),
            ('bigint_col', 'bigint'),
            ('float_col', 'float'),
            ('double_col', 'double'),
            ('date_string_col', 'string'),
            ('string_col', 'string'),
            ('timestamp_col', 'timestamp'),
            ('year', 'int'),
            ('month', 'int')],
 'storage info': {'Bucket Columns': '[]',
                  'Compressed': False,
                  'InputFormat': 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat',
                  'Num Buckets': 0,
                  'OutputFormat': 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat',
                  'SerDe Library': 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe',
                  'Sort Columns': '[]'}}

>>> meta.location
'hdfs://impala:8020/__ibis/ibis-testing-data/parquet/functional_alltypes'

>>> meta.create_time
datetime.datetime(2021, 1, 14, 21, 23, 8)

The files function is also available to see all of the physical HDFS data files backing a table:

files

files(['self'])

Return results of SHOW FILES statement.

>>> ss = c.table('tpcds_parquet.store_sales')

>>> ss.files()[:5]
                                                path      size  \
0  hdfs://localhost:20500/test-warehouse/tpcds.st...  160.61KB
1  hdfs://localhost:20500/test-warehouse/tpcds.st...  123.88KB
2  hdfs://localhost:20500/test-warehouse/tpcds.st...  139.28KB
3  hdfs://localhost:20500/test-warehouse/tpcds.st...  139.60KB
4  hdfs://localhost:20500/test-warehouse/tpcds.st...   62.84KB

                 partition
0  ss_sold_date_sk=2451803
1  ss_sold_date_sk=2451819
2  ss_sold_date_sk=2451772
3  ss_sold_date_sk=2451789
4  ss_sold_date_sk=2451741

Modifying table metadata

For unpartitioned tables, you can use the alter method to change its location, file format, and other properties. For partitioned tables, to change partition-specific metadata use alter_partition.

alter

alter(['self', 'location=None', 'format=None', 'tbl_properties=None', 'serde_properties=None'])

Change settings and parameters of the table.

Parameters

Name	Description	Default
location	For partitioned tables, you may want the alter_partition function	`None`
format	Table format	`None`
tbl_properties	Table properties	`None`
serde_properties	Serialization/deserialization properties	`None`

alter_partition

alter_partition(['self', 'spec', 'location=None', 'format=None', 'tbl_properties=None', 'serde_properties=None'])

Change settings and parameters of an existing partition.

Parameters

Name	Description	Default
spec	The partition keys for the partition being modified	required
location	Location of the partition	`None`
format	Table format	`None`
tbl_properties	Table properties	`None`
serde_properties	Serialization/deserialization properties	`None`

For example, if you wanted to "point" an existing table at a directory of CSV files, you could run the following command:

from getpass import getuser

csv_props = {
    'serialization.format': ',',
    'field.delim': ',',
}
data_dir = '/home/{}/my-csv-files'.format(getuser())

table.alter(location=data_dir, format='text', serde_properties=csv_props)

If the table is partitioned, you can modify only the properties of a particular partition:

table.alter_partition(
    {'year': 2007, 'month': 5},
    location=data_dir,
    format='text',
    serde_properties=csv_props
)

Table statistics

Computing table and partition statistics

compute_stats

compute_stats(['self', 'incremental=False'])

Invoke Impala COMPUTE STATS command on the table.

Impala-backed physical tables have a method compute_stats that computes table, column, and partition-level statistics to assist with query planning and optimization. It is standard practice to invoke this after creating a table or loading new data:

table.compute_stats()

If you are using a recent version of Impala, you can also access the COMPUTE INCREMENTAL STATS DDL command:

table.compute_stats(incremental=True)

Seeing table and column statistics

column_stats

column_stats(['self'])

Return results of SHOW COLUMN STATS.

Returns

Name	Type	Description
	DataFrame	Column statistics

stats

stats(['self'])

Return results of SHOW TABLE STATS.

If not partitioned, contains only one row.

Returns

Name	Type	Description
	DataFrame	Table statistics

The compute_stats and stats functions return the results of SHOW COLUMN STATS and SHOW TABLE STATS, respectively, and their output will depend, of course, on the last COMPUTE STATS call.

>>> ss = c.table('tpcds_parquet.store_sales')
>>> ss.compute_stats(incremental=True)
>>> stats = ss.stats()
>>> stats[:5]
  ss_sold_date_sk  #Rows  #Files     Size Bytes Cached Cache Replication  \
0         2450829   1071       1  78.34KB   NOT CACHED        NOT CACHED
1         2450846    839       1  61.83KB   NOT CACHED        NOT CACHED
2         2450860    747       1  54.86KB   NOT CACHED        NOT CACHED
3         2450874    922       1  66.74KB   NOT CACHED        NOT CACHED
4         2450888    856       1  63.33KB   NOT CACHED        NOT CACHED

    Format Incremental stats  \
0  PARQUET              true
1  PARQUET              true
2  PARQUET              true
3  PARQUET              true
4  PARQUET              true

                                            Location
0  hdfs://localhost:20500/test-warehouse/tpcds.st...
1  hdfs://localhost:20500/test-warehouse/tpcds.st...
2  hdfs://localhost:20500/test-warehouse/tpcds.st...
3  hdfs://localhost:20500/test-warehouse/tpcds.st...
4  hdfs://localhost:20500/test-warehouse/tpcds.st...

>>> cstats = ss.column_stats()
>>> cstats
                   Column          Type  #Distinct Values  #Nulls  Max Size  Avg Size
0         ss_sold_time_sk        BIGINT             13879      -1       NaN         8
1              ss_item_sk        BIGINT             17925      -1       NaN         8
2          ss_customer_sk        BIGINT             15207      -1       NaN         8
3             ss_cdemo_sk        BIGINT             16968      -1       NaN         8
4             ss_hdemo_sk        BIGINT              6220      -1       NaN         8
5              ss_addr_sk        BIGINT             14077      -1       NaN         8
6             ss_store_sk        BIGINT                 6      -1       NaN         8
7             ss_promo_sk        BIGINT               298      -1       NaN         8
8        ss_ticket_number           INT             15006      -1       NaN         4
9             ss_quantity           INT                99      -1       NaN         4
10      ss_wholesale_cost  DECIMAL(7,2)             10196      -1       NaN         4
11          ss_list_price  DECIMAL(7,2)             19393      -1       NaN         4
12         ss_sales_price  DECIMAL(7,2)             15594      -1       NaN         4
13    ss_ext_discount_amt  DECIMAL(7,2)             29772      -1       NaN         4
14     ss_ext_sales_price  DECIMAL(7,2)            102758      -1       NaN         4
15  ss_ext_wholesale_cost  DECIMAL(7,2)            125448      -1       NaN         4
16      ss_ext_list_price  DECIMAL(7,2)            141419      -1       NaN         4
17             ss_ext_tax  DECIMAL(7,2)             33837      -1       NaN         4
18          ss_coupon_amt  DECIMAL(7,2)             29772      -1       NaN         4
19            ss_net_paid  DECIMAL(7,2)            109981      -1       NaN         4
20    ss_net_paid_inc_tax  DECIMAL(7,2)            132286      -1       NaN         4
21          ss_net_profit  DECIMAL(7,2)            122436      -1       NaN         4
22        ss_sold_date_sk        BIGINT               120       0       NaN         8

`REFRESH` and `INVALIDATE METADATA`

These DDL commands are available as table-level and client-level methods:

invalidate_metadata

invalidate_metadata(['self', 'name=None', 'database=None'])

Issue an INVALIDATE METADATA command.

Optionally this applies to a specific table. See Impala documentation.

Parameters

Name	Type	Description	Default
name	str \| None	Table name. Can be fully qualified (with database)	`None`
database	str \| None	Database name	`None`

invalidate_metadata

invalidate_metadata(['self'])

refresh

refresh(['self'])

You can invalidate the cached metadata for a single table or for all tables using invalidate_metadata, and similarly invoke REFRESH db_name.table_name using the refresh method.

client.invalidate_metadata()

table = db.table(table_name)
table.invalidate_metadata()

table.refresh()

These methods are often used in conjunction with the LOAD DATA commands and COMPUTE STATS. See the Impala documentation for full details.

Parquet and other session options

Ibis gives you access to Impala session-level variables that affect query execution:

get_options

get_options(['self'])

Return current query options for the Impala session.

set_options

set_options(['self', 'options'])

set_compression_codec

set_compression_codec(['self', 'codec'])

For example:

>>> client.get_options()
{'ABORT_ON_ERROR': '0',
 'APPX_COUNT_DISTINCT': '0',
 'BUFFER_POOL_LIMIT': '',
 'COMPRESSION_CODEC': '',
 'COMPUTE_STATS_MIN_SAMPLE_SIZE': '1073741824',
 'DEFAULT_JOIN_DISTRIBUTION_MODE': '0',
 'DEFAULT_SPILLABLE_BUFFER_SIZE': '2097152',
 'DISABLE_ROW_RUNTIME_FILTERING': '0',
 'DISABLE_STREAMING_PREAGGREGATIONS': '0',
 'DISABLE_UNSAFE_SPILLS': '0',
 'ENABLE_EXPR_REWRITES': '1',
 'EXEC_SINGLE_NODE_ROWS_THRESHOLD': '100',
 'EXEC_TIME_LIMIT_S': '0',
 'EXPLAIN_LEVEL': '1',
 'HBASE_CACHE_BLOCKS': '0',
 'HBASE_CACHING': '0',
 'IDLE_SESSION_TIMEOUT': '0',
 'MAX_ERRORS': '100',
 'MAX_NUM_RUNTIME_FILTERS': '10',
 'MAX_ROW_SIZE': '524288',
 'MEM_LIMIT': '0',
 'MIN_SPILLABLE_BUFFER_SIZE': '65536',
 'MT_DOP': '',
 'NUM_SCANNER_THREADS': '0',
 'OPTIMIZE_PARTITION_KEY_SCANS': '0',
 'PARQUET_ANNOTATE_STRINGS_UTF8': '0',
 'PARQUET_ARRAY_RESOLUTION': '2',
 'PARQUET_DICTIONARY_FILTERING': '1',
 'PARQUET_FALLBACK_SCHEMA_RESOLUTION': '0',
 'PARQUET_FILE_SIZE': '0',
 'PARQUET_READ_STATISTICS': '1',
 'PREFETCH_MODE': '1',
 'QUERY_TIMEOUT_S': '0',
 'REPLICA_PREFERENCE': '0',
 'REQUEST_POOL': '',
 'RUNTIME_BLOOM_FILTER_SIZE': '1048576',
 'RUNTIME_FILTER_MAX_SIZE': '16777216',
 'RUNTIME_FILTER_MIN_SIZE': '1048576',
 'RUNTIME_FILTER_MODE': '2',
 'RUNTIME_FILTER_WAIT_TIME_MS': '0',
 'S3_SKIP_INSERT_STAGING': '1',
 'SCHEDULE_RANDOM_REPLICA': '0',
 'SCRATCH_LIMIT': '-1',
 'SEQ_COMPRESSION_MODE': '',
 'SYNC_DDL': '0'}

To enable Snappy compression for Parquet files, you could do either of:

>>> client.set_options({'COMPRESSION_CODEC': 'snappy'})
>>> client.set_compression_codec('snappy')

>>> client.get_options()['COMPRESSION_CODEC']
'SNAPPY'

Ingesting data from pandas

Overall interoperability between the Hadoop / Spark ecosystems and pandas / the PyData stack is poor, but it will improve in time (this is a major part of the Ibis roadmap).

Ibis’s Impala tools currently interoperate with pandas in these ways:

Ibis expressions return pandas objects (i.e. DataFrame or Series) for non-scalar expressions when calling their to_pandas method
The create_table and insert methods can accept pandas objects. This includes inserting into partitioned tables. It currently uses CSV as the ingest route.

For example:

>>> import pandas as pd

>>> data = pd.DataFrame({'foo': [1, 2, 3, 4], 'bar': ['a', 'b', 'c', 'd']})

>>> db.create_table('pandas_table', data)
>>> t = db.pandas_table
>>> t.to_pandas()
  bar  foo
0   a    1
1   b    2
2   c    3
3   d    4

>>> t.drop()

>>> db.create_table('empty_for_insert', schema=t.schema())

>>> to_insert = db.empty_for_insert
>>> to_insert.insert(data)
>>> to_insert.to_pandas()
  bar  foo
0   a    1
1   b    2
2   c    3
3   d    4

>>> to_insert.drop()

>>> import pandas as pd

>>> data = pd.DataFrame({'foo': [1, 2, 3, 4], 'bar': ['a', 'b', 'c', 'd']})

>>> db.create_table('pandas_table', data)
>>> t = db.pandas_table
>>> t.to_pandas()
   foo bar
0    1   a
1    2   b
2    3   c
3    4   d

>>> t.drop()
>>> db.create_table('empty_for_insert', schema=t.schema())
>>> to_insert = db.empty_for_insert
>>> to_insert.insert(data)
>>> to_insert.to_pandas()
   foo bar
0    1   a
1    2   b
2    3   c
3    4   d

>>> to_insert.drop()

Queries on Parquet, Avro, and Delimited files

Ibis can easily create temporary or persistent Impala tables that reference data in the following formats:

Parquet (parquet_file)
Avro (avro_file)
Delimited text formats (CSV, TSV, etc.) (delimited_file)

Parquet is the easiest because the schema can be read from the data files:

>>> path = '/__ibis/ibis-testing-data/parquet/tpch_lineitem'
>>> lineitem = con.parquet_file(path)
>>> lineitem.limit(2)
   l_orderkey  l_partkey  l_suppkey  l_linenumber l_quantity l_extendedprice  \
0           1     155190       7706             1      17.00        21168.23
1           1      67310       7311             2      36.00        45983.16

  l_discount l_tax l_returnflag l_linestatus  l_shipdate l_commitdate  \
0       0.04  0.02            N            O  1996-03-13   1996-02-12
1       0.09  0.06            N            O  1996-04-12   1996-02-28

  l_receiptdate     l_shipinstruct l_shipmode  \
0    1996-03-22  DELIVER IN PERSON      TRUCK
1    1996-04-20   TAKE BACK RETURN       MAIL

                            l_comment
0             egular courts above the
1  ly final dependencies: slyly bold

>>> lineitem.l_extendedprice.sum()
Decimal('229577310901.20')

If you want to query a Parquet file and also create a table in Impala that remains after your session, you can pass more information to parquet_file:

>>> table = con.parquet_file(path, name='my_parquet_table',
...                          database='ibis_testing',
...                          persist=True)
>>> table.l_extendedprice.sum()
Decimal('229577310901.20')

>>> con.table('my_parquet_table').l_extendedprice.sum()
Decimal('229577310901.20')

>>> con.drop_table('my_parquet_table')

To query delimited files, you need to write down an Ibis schema.

>>> schema = ibis.schema(dict(foo='string', bar='double', baz='int32'))
>>> table = con.delimited_file('/__ibis/ibis-testing-data/csv', schema)
>>> table.limit(10)
          foo       bar  baz
0  63IEbRheTh  0.679389    6
1  mG4hlqnjeG  2.807106   15
2  JTPdX9SZH5 -0.155126   55
3  2jcl6FypOl  1.037878   21
4  k3TbJLaadQ -1.401908   23
5  rP5J4xvinM -0.442093   22
6  WniUylixYt -0.863748   27
7  znsDuKOB1n -0.566030   47
8  4SRP9jlo1M  0.331460   88
9  KsfjPyDf5e -0.578931   70

>>> table.bar.summary()
   count  nulls       min       max       sum    mean  approx_nunique
0    100      0 -1.401908  2.807106  8.479978  0.0848              10

For functions like parquet_file and delimited_file, a directory must be passed and the directory must contain files all having the same schema.

Other helper functions for interacting with the database

We’re adding a growing list of useful utility functions for interacting with an Impala cluster on the client object. The idea is that you should be able to do any database-admin-type work with Ibis and not have to switch over to the Impala SQL shell. Any ways we can make this more pleasant, please let us know.

Here’s some of the features, which we’ll give examples for:

Listing and searching for available databases and tables
Creating and dropping databases
Getting table schemas

>>> con.list_databases(like='ibis*')
['ibis_testing', 'ibis_testing_tmp_db']

>>> con.list_tables(database='ibis_testing', like='tpch*')
['tpch_customer',
 'tpch_lineitem',
 'tpch_nation',
 'tpch_orders',
 'tpch_part',
 'tpch_partsupp',
 'tpch_region',
 'tpch_region_avro',
 'tpch_supplier']

>>> schema = con.get_schema('functional_alltypes')
>>> schema
ibis.Schema {
  id               int32
  bool_col         boolean
  tinyint_col      int8
  smallint_col     int16
  int_col          int32
  bigint_col       int64
  float_col        float32
  double_col       float64
  date_string_col  string
  string_col       string
  timestamp_col    timestamp
  year             int32
  month            int32
}

Databases can be created, too, and you can set the storage path in HDFS you want for the data files

>>> db = 'ibis_testing2'
>>> con.create_database(db, force=True)

>>> con.create_table('example_table', con.table('functional_alltypes'),
...                  database=db, force=True)

To drop a database, including all tables in it, you can use drop_database with force=True:

>>> con.drop_database(db, force=True)

User Defined functions (UDF)

Impala currently supports user-defined scalar functions (known henceforth as UDFs) and aggregate functions (respectively UDAs) via a C++ extension API.

Initial support for using C++ UDFs in Ibis came in version 0.4.0.

Using scalar functions (UDFs)

Let’s take an example to illustrate how to make a C++ UDF available to Ibis. Here is a function that computes an approximate equality between floating point values:

#include "impala_udf/udf.h"

#include <cctype>
#include <cmath>

BooleanVal FuzzyEquals(FunctionContext* ctx, const DoubleVal& x, const DoubleVal& y) {
  const double EPSILON = 0.000001f;
  if (x.is_null || y.is_null) return BooleanVal::null();
  double delta = fabs(x.val - y.val);
  return BooleanVal(delta < EPSILON);
}

You can compile this to either a shared library (a .so file) or to LLVM bitcode with clang (a .ll file). Skipping that step for now (will add some more detailed instructions here later, promise).

To make this function callable, we use ibis.impala.wrap_udf:

library = '/ibis/udfs/udftest.ll'
inputs = ['double', 'double']
output = 'boolean'
symbol = 'FuzzyEquals'
udf_db = 'ibis_testing'
udf_name = 'fuzzy_equals'

fuzzy_equals = ibis.impala.wrap_udf(
    library, inputs, output, symbol, name=udf_name
)

In typical workflows, you will set up a UDF in Impala once then use it thenceforth. So the first time you do this, you need to create the UDF in Impala:

client.create_function(fuzzy_equals, database=udf_db)

Now, we must register this function as a new Impala operation in Ibis. This must take place each time you load your Ibis session.

func.register(fuzzy_equals.name, udf_db)

The object fuzzy_equals is callable and works with Ibis expressions:

>>> t = con.tables.functional_alltypes

>>> expr = fuzzy_equals(t.float_col, t.double_col / 10)

>>> expr.to_pandas()[:10]
0     True
1    False
2    False
3    False
4    False
5    False
6    False
7    False
8    False
9    False
Name: tmp, dtype: bool

Note that the call to register on the UDF object must happen each time you use Ibis. If you have a lot of UDFs, I suggest you create a file with all of your wrapper declarations and user APIs that you load with your Ibis session to plug in all your own functions.

Working with secure clusters (Kerberos)

Ibis is compatible with Hadoop clusters that are secured with Kerberos (as well as SSL and LDAP). Note that to enable this support, you’ll also need to install the kerberos package.

$ pip install kerberos

Just like the Impala shell and ODBC/JDBC connectors, Ibis connects to Impala through the HiveServer2 interface (using the impyla client). Therefore, the connection semantics are similar to the other access methods for working with secure clusters.

Specifically, after authenticating yourself against Kerberos (e.g., by issuing the appropriate kinit command), pass auth_mechanism='GSSAPI' or auth_mechanism='LDAP' (and set kerberos_service_name if necessary along with user and password if necessary) to the ibis.impala_connect(...) method. This method also takes arguments to configure SSL (use_ssl, ca_cert). See the documentation for the Impala shell for more details.

Install

Database methods

create_database

Parameters

drop_database

Parameters

list_databases

Table methods

table

Parameters

Returns

sql

raw_sql

list_tables

Parameters

Returns

drop_table

Parameters

Examples

insert

Examples

truncate_table

Parameters

get_schema

Parameters

Returns

cache_table

Parameters

Examples

drop

insert

Parameters

Examples

describe_formatted

Creating views

drop_table_or_view

create_view

Parameters

Returns

Accessing data

delimited_file

Parameters

Returns

parquet_file

Parameters

Returns

avro_file

Parameters

Returns

The Impala client object

Table objects

table

Parameters

Returns

Expression execution

Creating tables

create_table

Parameters

Creating tables from a table expression

Creating an empty table

Creating a partitioned table

Partitioned tables

add_partition

drop_partition

is_partitioned

partition_schema

partitions

Inserting data into tables

Managing table metadata

Detailed table metadata: DESCRIBE FORMATTED

metadata

files

Modifying table metadata

alter

Parameters

alter_partition

Parameters

Table statistics

Computing table and partition statistics

compute_stats

Detailed table metadata: `DESCRIBE FORMATTED`

`REFRESH` and `INVALIDATE METADATA`