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TABLE I GROWTH OF POSTGRESQL
TABLE II SUMMARY OF ORIGIN ANALYSIS RESULTS FOR POSTGRESQL
TABLE IV OCCURRENCES OF PATTERN INSTANCES FOUND IN POSTGRESQL.
Table 3: Comparison of MySQL and PostgreSQL Features
2001
"... In PAGE 6: ...able 2: Linux Distributions ...................................................................................................... 14 Table3 : Comparison of MySQL and PostgreSQL Features .... ..."
Table 1 Overhead of Capturing Triggers PostgreSQL INSERT 126%
"... In PAGE 10: ... No other load was present to obtain stable results. Table1 shows that the overhead per update statement can be as high as 61%. These figures, however, need to be prorated with the overall load as the overhead is per update statement.... ..."
Table 6: Summary of TPC-D results using PostgreSQL. A portion of the TPC-D benchmark running on the PostgreSQL database system modified to use Active Disks.
2000
"... In PAGE 12: ...4. Benchmark Evaluation Table6 shows the details of a number of the queries from the TPC-D benchmark and shows the selectivity savings possible if the various processing steps are performed at the disks. Simply using the disks for the scan primitive to eliminate table columns that are not needed in the query reduces data by a factor of three on average.... In PAGE 16: ...4. Database - Summary Table6 summarizes the results of the last several sections and compares the performance of the server system and the Active Disk prototype on several of the most expensive queries from the TPC-D benchmark. We see that the scan-intensive applications, including the 2-way join, show linear scalability, while the more complex joins have significantly higher serial overheads, but still show significant speedups with Active Disk processing.... ..."
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Table 6: Summary of TPC-D results using PostgreSQL. A portion of the TPC-D benchmark running on the PostgreSQL database system modified to use Active Disks.
2000
"... In PAGE 12: ...4. Benchmark Evaluation Table6 shows the details of a number of the queries from the TPC-D benchmark and shows the selectivity savings possible if the various processing steps are performed at the disks. Simply using the disks for the scan primitive to eliminate table columns that are not needed in the query reduces data by a factor of three on average.... In PAGE 16: ...4. Database - Summary Table6 summarizes the results of the last several sections and compares the performance of the server system and the Active Disk prototype on several of the most expensive queries from the TPC-D benchmark. We see that the scan-intensive applications, including the 2-way join, show linear scalability, while the more complex joins have significantly higher serial overheads, but still show significant speedups with Active Disk processing.... ..."
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Table 4: Performance of two homogeneous and two heterogeneous queries on modified PostgreSQL (seconds)
2004
"... In PAGE 19: ...a. 1500 chunks. In all our experiments the ChunkCache size was set to 8 chunks (16 MB). Experimental results The experiments presented in Table4 consist of two runs. The Homoge- neousRun runs of two identical disk-bound instances of Query 1 (selectivity of the first step is ca.... In PAGE 19: ... We compare 3 different execution strategies: an unmodified PostgreSQL approach, and attach and relevance strategies presented in Section 3. As the upper part of Table4 shows, for the homogeneous queries all approaches behave equally well in the synchronized scenario. However, with queries de-synchronized, the traditional PostgreSQL... ..."
Table 7. PostgreSQL configuration parameters and their P amp;B values used.
2002
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