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In the late 1990s and early 2000s, university researchers began to focus on the problems of event processing and built prototypes of a different type of database engine, designed to directly process data in motion. These stream databases examine event data as it arrives, and calculate and summarize it on the fly, without persisting it to disk, as shown in Figure 26-3. Queries operate continuously, producing a steady stream of real-time moving averages or totals, or flagging individual events that are abnormal and require further examination. In some of these systems, the stream data can be combined with data in conventional database tables, which may contain reference data that is looked up to interpret the data stream or normal values against which the data stream is to be compared. This produces a hybrid database that combines event processing and conventional database analytics.

You can, of course, specify all columns in the INSERT INTO clause. If you do this, you must be sure to specify the same number of values, in the same order in which the columns are specified. The following INSERT statement inserts values into all columns of the CD_INVENTORY table:

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The concept of a continuous query is fundamental to the operation of stream databases. Unlike a traditional database query, which retrieves and calculates the specified data from data stored on disk as of a particular time, a continuous query is executed repeatedly against the data arriving in a data stream. Most products offer the ability to specify either time-based or record-based windows. They may also offer the choice of a sliding window or a jumping window. The operation of continuous queries and windows is illustrated in Figure 26-4. Using a time-based window, for example, a streaming database can be instructed to calculate the average price and total share volume for each stock traded during a fiveminute interval. The sequence of query values over time represents the state of the stock market during a trading session. Alternatively, using a record-based window, the database can be instructed to calculate the average price and total share volume for each group of 100 trades that occurs. Again, the sequence of query values over time will show the evolving state of the stock market, this time paced by the rate of trading instead of the passage of time. In all cases, the query results often represent statistical calculations of numerical data (totals, averages, maximum, minimum, standard deviation, etc.) during a window of time, possibly grouped (as in this example, by ticker symbol of the stock). It is often useful to combine this dynamically calculated information with static information that is looked up in a traditional enterprise database.

Part VI:

INSERT INTO CD_INVENTORY ( CD_NAME, MUSIC_TYPE, PUBLISHER, IN_STOCK ) VALUES ( 'Court and Spark', 'Pop', 'Asylum Records', 22 );

Time-based windows (every 50 milliseconds)

When you execute this statement, a row is added to the CD_INVENTORY table, with a value for each column. Figure 8-5 shows the new row, along with the other four rows we have inserted. If you were to omit one of the values from the VALUES clause even if null values were allowed for the related column you would receive an error when you executed that statement.

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Record-based windows (every 5 records)

Earlier in this chapter, at the beginning of the Insert SQL Data section, I say that the VALUES clause is mandatory and that you need to specify at least one value. There is, however, an alternative to the VALUES clause. You can use a SELECT statement to specify the values that

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FIGURE 26-4

8:

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