Impala with HBase

You can use Impala to query data residing in HBase tables, a key-value data store where the value consists of multiple fields. The key is mapped to one column in the Impala table, and the various fields of the value are mapped to the other columns in the Impala table.

HBase tables are the best suited in Impala in the following use cases.

  • Storing rapidly incrementing counters, such as how many times a web page has been viewed, or on a social network, how many connections a user has or how many votes a post received.

    The append-only storage mechanism of HBase is efficient for writing each change to disk, and a query always returns the latest value. An application could query specific totals like these from HBase, and combine the results with a broader set of data queried from Impala.

  • Storing very wide tables in HBase.

    Wide tables have many columns, possibly thousands, typically recording many attributes for an important subject such as a user of an online service. These tables are also often sparse, that is, most of the columns values are NULL, 0, false, empty string, or other blank or placeholder value. (For example, any particular web site user might have never used some site feature, filled in a certain field in their profile, visited a particular part of the site, and so on.) A typical query against this kind of table is to look up a single row to retrieve all the information about a specific subject, rather than summing, averaging, or filtering millions of rows as in typical Impala-managed tables.

HBase works out of the box with Impala. There is no mandatory configuration needed to use these two components together.

For efficient queries, use WHERE clauses to find a single key value or a range of key values wherever practical, by testing the Impala column corresponding to the HBase row key. Avoid queries that do full-table scans, which are efficient for regular Impala tables but inefficient in HBase.

To work with an HBase table from Impala, ensure that the impala user has read/write privileges for the HBase table, using the GRANT command in the HBase shell.

Creating HBase Tables for Impala

You create the tables on the Impala side using the Hive shell, because the Impala CREATE TABLE statement currently does not support custom SerDes and some other syntax needed for HBase tables.
  • You create the new table as an HBase table using the STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' clause on the Hive CREATE TABLE statement.
  • You map these specially created tables to corresponding tables that exist in HBase, with the clause TBLPROPERTIES("hbase.table.name" = "table_name_in_hbase") on the Hive CREATE TABLE statement.
  • You define the column corresponding to the HBase row key as a string with the #string keyword, or map it to a STRING column.
  • After creating a new table through Hive, issue the INVALIDATE METADATA statement in Impala to make Impala aware of the new table.

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 against the column that corresponds to the HBase row key.
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.