Impala with HBase

Creating HBase Tables for Impala

Because Impala and Hive share the same Metastore database, once you create the table in Hive, you can query or insert into it through Impala.

Supported Data Types for HBase Columns

HBase does not enforce any typing for the key or value fields. All the type enforcement is done on the Impala side.

HBase row key

When creating the table through the Hive shell, use the STRING data type for the column that corresponds to the HBase row key.

For best performance of Impala queries against HBase tables, most queries will perform comparisons in the WHERE clause against the column that corresponds to the HBase row key.

Impala can translate predicates (through operators such as =, <, and BETWEEN) against this column into fast lookups in HBase, but this optimization (predicate pushdown) only works when that column is defined as STRING.

HBase numeric column

Define HBase numeric columns as the binary data type in Hive CREATE TABLE statement using the #binary (#b) keyword.

Defining numeric columns as binary can reduce the overall data volume in the HBase tables.

Impala also supports reading and writing to columns that are defined in the Hive CREATE TABLE statement using binary data types , represented in the Hive table definition using the #binary keyword, often abbreviated as #b.

Loading Data into HBase Tables

The Impala INSERT statement supports HBase tables. The INSERT ... VALUES syntax is ideally suited to HBase tables, because inserting a single row is an efficient operation for an HBase table.

When you use the INSERT ... SELECT syntax, the result in the HBase table could be fewer rows than you expect. HBase only stores the most recent version of each unique row key, so if an INSERT ... SELECT statement copies over multiple rows containing the same value for the key column, subsequent queries will only return one row with each key column value.

Successive INSERT statements using the same value for the key column achieves the same result as UPDATE.

Examples of Querying HBase Tables from Impala

The following examples create an HBase table with four column families, create a corresponding table through Hive, then insert and query the table through Impala.

In HBase, create a table. Table names are quoted in the CREATE statement in HBase.

hbase(main):001:0> CREATE 'hbasealltypessmall', 'boolsCF', 'intsCF', 'floatsCF', 'stringsCF'

Issue the following CREATE TABLE statement in the Hive shell.

This example creates an external table mapped to the HBase table, usable by both Impala and Hive. It is defined as an external table so that when dropped by Impala or Hive, the original HBase table is not touched at all.

The WITH SERDEPROPERTIES clause specifies that the first column (ID) represents the row key, and maps the remaining columns of the SQL table to HBase column families. The mapping relies on the ordinal order of the columns in the table, not the column names in the CREATE TABLE statement. The first column is defined to be the lookup key; the STRING data type produces the fastest key-based lookups for HBase tables.

hive> CREATE EXTERNAL TABLE hbasestringids (
    >   id string,
    >   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)
    > STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler'
    > WITH SERDEPROPERTIES (
    >   "hbase.columns.mapping" =
    >   ":key,boolsCF:bool_col,intsCF:tinyint_col,intsCF:smallint_col,intsCF:int_col,intsCF:\
    >   bigint_col,floatsCF:float_col,floatsCF:double_col,stringsCF:date_string_col,\
    >   stringsCF:string_col,stringsCF:timestamp_col"
    > )
    > TBLPROPERTIES("hbase.table.name" = "hbasealltypessmall");

Once you have established the mapping to an HBase table, you can issue DML statements and queries from Impala. The following example shows a series of INSERT statements followed by a query. The ideal kind of query from a performance standpoint retrieves a row from the table based on a row key mapped to a string column.

The INVALIDATE METADATA statement makes the table created through Hive visible to Impala.

[impala] > INVALIDATE METADATA hbasestringids;

[impala] > INSERT INTO hbasestringids VALUES ('0001',true,3.141,9.94,1234567,32768,4000,76,'2014-12-31','Hello world',NOW());

[impala] > IMSERT INTO hbasestringids VALUES ('0002',false,2.004,6.196,1500,8000,129,127,'2014-01-01','Foo bar',NOW());

[impala] > SELECT * FROM hbasestringids WHERE id = '0001';
+------+----------+------------+-------------------+------------+---------+--------------+-------------+-----------------+-------------+-------------------------------+
| id   | bool_col | double_col | float_col         | bigint_col | int_col | smallint_col | tinyint_col | date_string_col | string_col  | timestamp_col                 |
+------+----------+------------+-------------------+------------+---------+--------------+-------------+-----------------+-------------+-------------------------------+
| 0001 | true     | 3.141      | 9.939999580383301 | 1234567    | 32768   | 4000         | 76          | 2014-12-31      | Hello world | 2015-02-10 16:36:59.764838000 |
+------+----------+------------+-------------------+------------+---------+--------------+-------------+-----------------+-------------+-------------------------------+

Performance Considerations for the Impala-HBase Integration

Impala uses the HBase client API via Java Native Interface (JNI) to query data stored in HBase. This querying does not read HFiles directly. The extra communication overhead makes it important to choose what data to store in HBase or in HDFS, and construct efficient queries that can retrieve the HBase data efficiently:

• Use HBase table for queries that return a single row or a small range of rows, not queries that perform a full table scan of an entire table. (If a query has a HBase table and no WHERE clause referencing that table, that is a strong indicator that it is an inefficient query for an HBase table.)
• HBase may offer acceptable performance for storing small dimension tables where the table is small enough that executing a full table scan for every query is efficient enough. However, Kudu is almost always a superior alternative for storing dimension tables. HDFS tables are also appropriate for dimension tables that do not need to support update queries, delete queries or insert queries with small numbers of rows.

Query predicates are applied to row keys as start and stop keys, thereby limiting the scope of a particular lookup. If row keys are not mapped to string columns, then ordering is typically incorrect and comparison operations do not work. For example, if row keys are not mapped to string columns, evaluating for greater than (>) or less than (<) cannot be completed.

Predicates on non-key columns can be sent to HBase to scan as SingleColumnValueFilters, providing some performance gains. In such a case, HBase returns fewer rows than if those same predicates were applied using Impala. While there is some improvement, it is not as great when start and stop rows are used. This is because the number of rows that HBase must examine is not limited as it is when start and stop rows are used. As long as the row key predicate only applies to a single row, HBase will locate and return that row. Conversely, if a non-key predicate is used, even if it only applies to a single row, HBase must still scan the entire table to find the correct result.

Limitations and Restrictions of the Impala and HBase Integration

The Impala integration with HBase has the following limitations and restrictions, some inherited from the integration between HBase and Hive, and some unique to Impala:

• If you issue a DROP TABLE for an internal (Impala-managed) table that is mapped to an HBase table, the underlying table is not removed in HBase. The Hive DROP TABLE statement removes the HBase table in this case.

• The INSERT OVERWRITE statement is not available for HBase tables. You can insert new data, or modify an existing row by inserting a new row with the same key value, but not replace the entire contents of the table. You can do an INSERT OVERWRITE in Hive if you need this capability.

• If you issue a CREATE TABLE LIKE statement for a table mapped to an HBase table, the new table is also an HBase table, but inherits the same underlying HBase table name as the original. The new table is effectively an alias for the old one, not a new table with identical column structure. Avoid using CREATE TABLE LIKE for HBase tables, to avoid any confusion.

• Copying data into an HBase table using the Impala INSERT ... SELECT syntax might produce fewer new rows than are in the query result set. If the result set contains multiple rows with the same value for the key column, each row supercedes any previous rows with the same key value. Because the order of the inserted rows is unpredictable, you cannot rely on this technique to preserve the latest version of a particular key value.

• The LOAD DATA statement cannot be used with HBase tables.

• The TABLESAMPLE clause of the SELECT statement does not suppport HBase tables.