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 Type 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.

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

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, validate=True)

Insert into an Impala table.

Parameters
Name Type Description Default
table_name The table name required
obj Table expression or DataFrame None
database The table database 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
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

>>> con.insert(table_name, table_expr)  # quartodoc: +SKIP

Completely overwrite contents

>>> con.insert(table_name, 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 Type 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

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 Type 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
Table 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
Table 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 Type 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
Table 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.

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')

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

>>> freqs = client.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 on the client object.

schema = ibis.schema(dict(foo='string', year='int32', month='int16'))
name = 'new_table'
client.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())
client.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 = client.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'
client.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 methods for adding and removing partitions and getting information about the partition schema and any existing partition data:

add_partition

add_partition(self, table_name, spec, *, database=None, 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.

Parameters
Name Type Description Default
table_name str The table name. required
spec dict[str, Any] | list The partition keys for the partition being added. required
database str | None The database name. If not provided, the current database is used. None
location str | None Location of the partition None

drop_partition

drop_partition(self, table_name, spec, *, database=None)

Drop an existing table partition.

Parameters
Name Type Description Default
table_name str The table name. required
spec dict[str, Any] | list The partition keys for the partition being dropped. required
database str | None The database name. If not provided, the current database is used. None

get_partition_schema

get_partition_schema(self, table_name, database=None)

Return the schema for the partition columns.

Parameters
Name Type Description Default
table_name str Table name required
database str | None Database name None
Returns
Name Type Description
Schema Ibis schema for the partition columns

list_partitions

list_partitions(self, name, database=None)

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'
client.create_table(name, schema=schema, partition=['year', 'month'])

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

client.drop_partition(name, [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 = client.functional_alltypes
>>> client.create_table('insert_test', schema=t.schema())

>>> client.insert('insert_test', t[:3])
>>> client.insert('insert_test', t[:3])
>>> client.insert('insert_test', t[:3])

>>> target = client.table('insert_test')


>>> 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]

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

part = {'year': 2007, 'month': 4}
client.insert(table_name, 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 describe_formatted method.

describe_formatted

describe_formatted(self, name, database=None)

Retrieve the results of a DESCRIBE FORMATTED command.

See Impala documentation for more.

Parameters
Name Type Description Default
name str Table name. Can be fully qualified (with database) required
database str | None Database name None 
>>> meta = t.describe_formatted('functional_alltypes', database='ibis_testing')
>>> 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 show_files function is also available to see all of the physical HDFS data files backing a table:

show_files

show_files(self, name, database=None)

Retrieve results of a SHOW FILES command for a table.

See Impala documentation for more.

Parameters
Name Type Description Default
name str Table name. Can be fully qualified (with database) required
database str | None Database name None 
>>> client.show_files('store_sales', database='tpcds_parquet')[: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

alter_partition

alter_partition(self, table_name, spec, *, database=None, location=None, format=None, tbl_properties=None, serde_properties=None)

Change settings and parameters of an existing partition.

Parameters
Name Type Description Default
table_name str The table name required
spec dict[str, Any] | list The partition keys for the partition being modified required
database str | None The database name. If not provided, the current database is used. None
location str | None Location of the partition None
format str | None Table format None
tbl_properties dict | None Table properties None
serde_properties dict | None Serialization/deserialization properties None

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

client.alter_partition(
    'table_name',
    {'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, name, database=None, incremental=False)

Issue a COMPUTE STATS command for a given table.

Parameters
Name Type Description Default
name str Can be fully qualified (with database name) required
database str | None Database name None
incremental bool If True, issue COMPUTE INCREMENTAL STATS False

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:

client.compute_stats('table_name')

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

client.compute_stats('table_name', incremental=True)

Seeing table and column statistics

column_stats

column_stats(self, name, database=None)

Return results of SHOW COLUMN STATS for the table name.

table_stats

table_stats(self, name, database=None)

Return results of SHOW TABLE STATS for the table name.

The compute_stats and table_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.

>>> client.compute_stats('store_sales', database='tpcds_parquet', incremental=True)
>>> stats = client.table_stats('store_sales', database='tpcds_parquet')
>>> 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 = client.column_status('store_sales', database='tpcds_parquet')
>>> 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 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

refresh

refresh(self, name, database=None)

Reload metadata for a table.

This can be useful after ingesting data as part of an ETL pipeline, for example.

Related to INVALIDATE METADATA. See Impala documentation for more.

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

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()

client.invalidate_metadata(table_name)

client.refresh(table_name)

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']})

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

>>> to_insert = client.create_table('empty_for_insert', schema=t.schema())

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

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.

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