hbase-8257. refGuide. Adding object design section in Cust/Order Schema Design Case Study.
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@ -438,7 +438,7 @@ public static byte[][] getHexSplits(String startKey, String endKey, int numRegio
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<itemizedlist>
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<listitem>Log Data / Timeseries Data</listitem>
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<listitem>Log Data / Timeseries on Steroids</listitem>
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<listitem>Customer/Sales</listitem>
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<listitem>Customer/Order</listitem>
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<listitem>Tall/Wide/Middle Schema Design</listitem>
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<listitem>List Data</listitem>
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</itemizedlist>
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@ -527,7 +527,7 @@ long bucket = timestamp % numBuckets;
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</para>
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</section> <!-- varkeys -->
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</section> <!-- log data and timeseries -->
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<section xml:id="schema.casestudies.log-timeseries.log-steroids">
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<section xml:id="schema.casestudies.log-steroids">
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<title>Case Study - Log Data and Timeseries Data on Steroids</title>
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<para>This effectively is the OpenTSDB approach. What OpenTSDB does is re-write data and pack rows into columns for
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certain time-periods. For a detailed explanation, see: <link xlink:href="http://opentsdb.net/schema.html">http://opentsdb.net/schema.html</link>,
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@ -549,10 +549,10 @@ long bucket = timestamp % numBuckets;
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</para>
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</section> <!-- log data timeseries steroids -->
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<section xml:id="schema.casestudies.log-timeseries.custsales">
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<title>Case Study - Customer / Sales</title>
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<para>Assume that HBase is used to store customer and sales information. There are two core record-types being ingested:
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a Customer record type, and Sales record type.
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<section xml:id="schema.casestudies.custorder">
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<title>Case Study - Customer/Order</title>
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<para>Assume that HBase is used to store customer and order information. There are two core record-types being ingested:
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a Customer record type, and Order record type.
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</para>
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<para>The Customer record type would include all the things that you’d typically expect:
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<itemizedlist>
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@ -562,21 +562,21 @@ long bucket = timestamp % numBuckets;
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<listitem>Phone numbers, etc.</listitem>
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</itemizedlist>
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</para>
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<para>The Sales record type would include things like:
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<para>The Order record type would include things like:
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<itemizedlist>
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<listitem>Customer number</listitem>
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<listitem>Sales/order number</listitem>
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<listitem>Order number</listitem>
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<listitem>Sales date</listitem>
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<listitem>A series of nested objects for shipping locations and line-items (this itself is a design case study)</listitem>
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<listitem>A series of nested objects for shipping locations and line-items (see <xref linkend="schema.casestudies.custorder.obj"/>
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for details)</listitem>
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</itemizedlist>
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</para>
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<para>Assuming that the combination of customer number and sales order uniquely identify an order, these two attributes will compose
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the rowkey, and specifically a composite key such as:
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</para>
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<para><code>[customer number][sales number]</code>
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<para><code>[customer number][order number]</code>
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</para>
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<para>
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… for a SALES table. However, there are more design decisions to make: are the <emphasis>raw</emphasis> values the best choices for rowkeys?
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<para>… for a ORDER table. However, there are more design decisions to make: are the <emphasis>raw</emphasis> values the best choices for rowkeys?
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</para>
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<para>The same design questions in the Log Data use-case confront us here. What is the keyspace of the customer number, and what is the
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format (e.g., numeric? alphanumeric?) As it is advantageous to use fixed-length keys in HBase, as well as keys that can support a
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@ -585,16 +585,16 @@ reasonable spread in the keyspace, similar options appear:
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<para>Composite Rowkey With Hashes:
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<itemizedlist>
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<listitem>[MD5 of customer number] = 16 bytes</listitem>
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<listitem>[MD5 of sales number] = 16 bytes</listitem>
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<listitem>[MD5 of order number] = 16 bytes</listitem>
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</itemizedlist>
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</para>
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<para>Composite Numeric/Hash Combo Rowkey:
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<itemizedlist>
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<listitem>[substituted long for customer number] = 8 bytes</listitem>
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<listitem>[MD5 of sales number] = 16 bytes</listitem>
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<listitem>[MD5 of order number] = 16 bytes</listitem>
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</itemizedlist>
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</para>
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<section xml:id="schema.casestudies.log-timeseries.custsales.tables">
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<section xml:id="schema.casestudies.custorder.tables">
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<title>Single Table? Multiple Tables?</title>
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<para>A traditional design approach would have separate tables for CUSTOMER and SALES. Another option is to pack multiple
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record types into a single table (e.g., CUSTOMER++).
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@ -605,11 +605,11 @@ reasonable spread in the keyspace, similar options appear:
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<listitem>[type] = type indicating ‘1’ for customer record type</listitem>
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</itemizedlist>
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</para>
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<para>Sales Record Type Rowkey:
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<para>Order Record Type Rowkey:
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<itemizedlist>
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<listitem>[customer-id]</listitem>
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<listitem>[type] = type indicating ‘2’ for sales record type</listitem>
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<listitem>[sales-order]</listitem>
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<listitem>[type] = type indicating ‘2’ for order record type</listitem>
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<listitem>[order]</listitem>
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</itemizedlist>
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</para>
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<para>The advantage of this particular CUSTOMER++ approach is that organizes many different record-types by customer-id
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@ -617,7 +617,121 @@ reasonable spread in the keyspace, similar options appear:
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a particular record-type.
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</para>
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</section>
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</section> <!-- cust/sales -->
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<section xml:id="schema.casestudies.custorder.obj">
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<title>Order Object Design</title>
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<para>Now we need to address how to model the Order object. Assume that the class structure is as follows:
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<programlisting>
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<filename>Order</filename>
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<filename>ShippingLocation</filename> (an Order can have multiple ShippingLocations)
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<filename>LineItem</filename> (a ShippingLocation can have multiple LineItems)
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</programlisting>
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... there are multiple options on storing this data.
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</para>
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<section xml:id="schema.casestudies.custorder.obj.norm">
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<title>Completely Normalized</title>
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<para>With this approach, there would be separate tables for ORDER, SHIPPING_LOCATION, and LINE_ITEM.
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</para>
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<para>The ORDER table's rowkey was described above: <xref linkend="schema.casestudies.custorder"/>
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</para>
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<para>The SHIPPING_LOCATION's composite rowkey would be something like this:
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<itemizedlist>
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<listitem>[order-rowkey]</listitem>
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<listitem>[shipping location number] (e.g., 1st location, 2nd, etc.)</listitem>
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</itemizedlist>
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</para>
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<para>The LINE_ITEM table's composite rowkey would be something like this:
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<itemizedlist>
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<listitem>[order-rowkey]</listitem>
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<listitem>[shipping location number] (e.g., 1st location, 2nd, etc.)</listitem>
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<listitem>[line item number] (e.g., 1st lineitem, 2nd, etc.)</listitem>
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</itemizedlist>
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</para>
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<para>Such a normalized model is likely to be the approach with an RDBMS, but that's not your only option with HBase.
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The cons of such an approach is that to retrieve information about any Order, you will need:
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<itemizedlist>
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<listitem>Get on the ORDER table for the Order</listitem>
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<listitem>Scan on the SHIPPING_LOCATION table for that order to get the ShippingLocation instances</listitem>
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<listitem>Scan on the LINE_ITEM for each ShippingLocation</listitem>
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</itemizedlist>
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... granted, this is what an RDBMS would do under the covers anyway, but since there are no joins in HBase
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you're just more aware of this fact.
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</para>
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</section>
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<section xml:id="schema.casestudies.custorder.obj.rectype">
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<title>Single Table With Record Types</title>
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<para>With this approach, there would exist a single table ORDER that would contain
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</para>
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<para>The Order rowkey was described above: <xref linkend="schema.casestudies.custorder"/>
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<itemizedlist>
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<listitem>[order-rowkey]</listitem>
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<listitem>[ORDER record type]</listitem>
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</itemizedlist>
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</para>
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<para>The ShippingLocation composite rowkey would be something like this:
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<itemizedlist>
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<listitem>[order-rowkey]</listitem>
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<listitem>[SHIPPING record type]</listitem>
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<listitem>[shipping location number] (e.g., 1st location, 2nd, etc.)</listitem>
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</itemizedlist>
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</para>
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<para>The LineItem composite rowkey would be something like this:
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<itemizedlist>
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<listitem>[order-rowkey]</listitem>
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<listitem>[LINE record type]</listitem>
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<listitem>[shipping location number] (e.g., 1st location, 2nd, etc.)</listitem>
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<listitem>[line item number] (e.g., 1st lineitem, 2nd, etc.)</listitem>
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</itemizedlist>
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</para>
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</section>
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<section xml:id="schema.casestudies.custorder.obj.denorm">
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<title>Denormalized</title>
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<para>A variant of the Single Table With Record Types approach is to denormalize and flatten some of the object
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hierarchy, such as collapsing the ShippingLocation attributes onto each LineItem instance.
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</para>
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<para>The LineItem composite rowkey would be something like this:
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<itemizedlist>
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<listitem>[order-rowkey]</listitem>
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<listitem>[LINE record type]</listitem>
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<listitem>[line item number] (e.g., 1st lineitem, 2nd, etc. - care must be taken that there are unique across the entire order)</listitem>
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</itemizedlist>
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</para>
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<para>... and the LineItem columns would be something like this:
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<itemizedlist>
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<listitem>itemNumber</listitem>
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<listitem>quantity</listitem>
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<listitem>price</listitem>
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<listitem>shipToLine1 (denormalized from ShippingLocation)</listitem>
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<listitem>shipToLine2 (denormalized from ShippingLocation)</listitem>
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<listitem>shipToCity (denormalized from ShippingLocation)</listitem>
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<listitem>shipToState (denormalized from ShippingLocation)</listitem>
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<listitem>shipToZip (denormalized from ShippingLocation)</listitem>
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</itemizedlist>
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</para>
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<para>The pros of this approach include a less complex object heirarchy, but one of the cons is that updating gets more
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complicated in case any of this information changes.
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</para>
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</section>
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<section xml:id="schema.casestudies.custorder.obj.singleobj">
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<title>Object BLOB</title>
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<para>With this approach, the entire Order object graph is treated, in one way or another, as a BLOB. For example, the
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ORDER table's rowkey was described above: <xref linkend="schema.casestudies.custorder"/>, and a
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single column called "order" would contain an object that could be deserialized that contained a container Order,
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ShippingLocations, and LineItems.
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</para>
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<para>There are many options here: JSON, XML, Java Serialization, Avro, Hadoop Writables, etc. All of them are variants
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of the same approach: encode the object graph to a byte-array. Care should be taken with this approach to ensure backward
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compatibilty in case the object model changes such that older persisted structures can still be read back out of HBase.
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</para>
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<para>Pros are being able to manage complex object graphs with minimal I/O (e.g., a single HBase Get per
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Order in this example), but the cons include the aforementioned warning about backward compatiblity of serialization,
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language dependencies of serialization (e.g., Java Serialization only works with Java clients), the fact that
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you have to deserialize the entire object to get any piece of information inside the BLOB, and the difficulty in
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getting frameworks like Hive to work with custom objects like this.
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</para>
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</section>
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</section> <!-- cust/order order object -->
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</section> <!-- cust/order -->
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<section xml:id="schema.smackdown"><title>Case Study - "Tall/Wide/Middle" Schema Design Smackdown</title>
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<para>This section will describe additional schema design questions that appear on the dist-list, specifically about
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tall and wide tables. These are general guidelines and not laws - each application must consider its own needs.
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