[impala] 01/05: IMPALA-8102: update Impala/HBase docs

[ph...@apache.org]

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commit 79e735a46df258395ea518a5cf6e22e851a91119
Author: Tim Armstrong <ta...@cloudera.com>
AuthorDate: Wed Jan 30 12:54:09 2019 -0800

    IMPALA-8102: update Impala/HBase docs
    
    Provide pointers to Kudu, which is generally better for analytics
    
    Remove or reword advice that encourages people to use HBase for
    analytics.
    
    Remove incorrect information about joins resulting in single-row HBase
    lookups - this simply doesn't happen.
    
    Change-Id: If1d5f014722d35eab9b60f7a4e8479738f1bed5b
    Reviewed-on: http://gerrit.cloudera.org:8080/12315
    Tested-by: Impala Public Jenkins <im...@cloudera.com>
    Reviewed-by: Alex Rodoni <ar...@cloudera.com>
---
 docs/topics/impala_hbase.xml | 45 ++++++++++++++++++--------------------------
 1 file changed, 18 insertions(+), 27 deletions(-)

diff --git a/docs/topics/impala_hbase.xml b/docs/topics/impala_hbase.xml
index 6c1822f..63f14af 100644
--- a/docs/topics/impala_hbase.xml
+++ b/docs/topics/impala_hbase.xml
@@ -37,11 +37,13 @@ under the License.
 
     <p>
       <indexterm audience="hidden">HBase</indexterm>
-      You can use Impala to query HBase tables. This capability allows convenient access to a storage system that
-      is tuned for different kinds of workloads than the default with Impala. The default Impala tables use data
-      files stored on HDFS, which are ideal for bulk loads and queries using full-table scans. In contrast, HBase
-      can do efficient queries for data organized for OLTP-style workloads, with lookups of individual rows or
-      ranges of values.
+      You can use Impala to query HBase tables. This is useful for accessing any of
+      your existing HBase tables via SQL and performing analytics over them. HDFS
+      and Kudu tables are preferred over HBase for analytic workloads and offer
+      superior performance. Kudu supports efficient inserts, updates and deletes
+      of small numbers of rows and can replace HBase for most analytics-oriented use
+      cases.  See <xref href="impala_kudu.xml#impala_kudu"/> for information on using
+      Impala with Kudu.
     </p>
 
     <p>
@@ -227,17 +229,19 @@ under the License.
 
       <ul>
         <li>
-          Use HBase table for queries that return a single row or a range of rows, not queries that scan the entire
-          table. (If a query has no <codeph>WHERE</codeph> clause, that is a strong indicator that it is an
-          inefficient query for an HBase table.)
+          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 <codeph>WHERE</codeph> clause referencing that table,
+          that is a strong indicator that it is an inefficient query for an HBase table.)
         </li>
 
         <li>
-          If you have join queries that do aggregation operations on large fact tables and join the results against
-          small dimension tables, consider using Impala for the fact tables and HBase for the dimension tables.
-          (Because Impala does a full scan on the HBase table in this case, rather than doing single-row HBase
-          lookups based on the join column, only use this technique where the HBase table is small enough that
-          doing a full table scan does not cause a performance bottleneck for the query.)
+          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.
         </li>
       </ul>
 
@@ -577,17 +581,11 @@ set hbase_caching=1000;
     <conbody>
 
       <p>
-        The following are popular use cases for using Impala to query HBase tables:
+        The following are representative use cases for using Impala to query HBase tables:
       </p>
 
       <ul>
         <li>
-          Keeping large fact tables in Impala, and smaller dimension tables in HBase. The fact tables use Parquet
-          or other binary file format optimized for scan operations. Join queries scan through the large Impala
-          fact tables, and cross-reference the dimension tables using efficient single-row lookups in HBase.
-        </li>
-
-        <li>
           Using HBase to store 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. HBase is
           efficient for capturing such changeable data: the append-only storage mechanism is efficient for writing
@@ -606,13 +604,6 @@ set hbase_caching=1000;
             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.
           </p>
-          <p>
-            Or the HBase table could be joined with a larger Impala-managed table. For example, analyze the large
-            Impala table representing web traffic for a site and pick out 50 users who view the most pages. Join
-            that result with the wide user table in HBase to look up attributes of those users. The HBase side of
-            the join would result in 50 efficient single-row lookups in HBase, rather than scanning the entire user
-            table.
-          </p>
         </li>
       </ul>
     </conbody>