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170 lines
7.5 KiB
Markdown
170 lines
7.5 KiB
Markdown
This is a quick comparison of three different approaches to load a big CSV/TSV file into a sqlite database.
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TL;DR sqlite has an `.import` function that works wonders.
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# Results
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## Python
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```
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❯ time python2 convert.py sample-twitter_rv.net
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Done: 9999999 lines. 131128444 / 131128444 bytes (100.0 %)
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python2 convert.py sample-twitter_rv.net 63.96s user 27.51s system 63% cpu 2:23.53 total
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```
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This is the output of cProfile:
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```
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python2 -m cProfile convert.py sample-twitter_rv.net
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Done: 9999999 lines. 131128444 / 131128444 bytes (100.0 %)
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50006080 function calls (50006073 primitive calls) in 241.581 seconds
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Ordered by: cumulative time
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ncalls tottime percall cumtime percall filename:lineno(function)
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1 0.000 0.000 241.581 241.581 convert.py:1(<module>)
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1 12.686 12.686 241.576 241.576 convert.py:18(main)
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10000000 5.114 0.000 157.256 0.000 convert.py:7(addusers)
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10000004 152.164 0.000 152.164 0.000 {method 'execute' of 'sqlite3.Connection' objects}
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1001 66.972 0.067 66.972 0.067 {method 'commit' of 'sqlite3.Connection' objects}
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10000002 2.471 0.000 2.471 0.000 {method 'split' of 'str' objects}
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10000000 1.103 0.000 1.103 0.000 {method 'strip' of 'str' objects}
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10000000 1.015 0.000 1.015 0.000 {len}
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1001 0.032 0.000 0.046 0.000 convert.py:12(update_progress)
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1001 0.011 0.000 0.011 0.000 {method 'format' of 'str' objects}
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1000 0.005 0.000 0.005 0.000 {method 'tell' of 'file' objects}
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1 0.000 0.000 0.005 0.005 __init__.py:24(<module>)
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1 0.002 0.002 0.004 0.004 dbapi2.py:24(<module>)
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2002 0.003 0.000 0.003 0.000 {method 'write' of 'file' objects}
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1 0.001 0.001 0.002 0.002 collections.py:11(<module>)
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```
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## Golang
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### With Transactions
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```
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❯ time ./twitter-sqlite sample-twitter_rv.net
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131128444/131128444 Bytes (100.000%) 10000000 lines - 0.00 Bps (avg. 1656955.51 Bps)
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./sqlite-twitter sample-twitter_rv.net 71.16s user 24.15s system 120% cpu 1:19.15 total
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```
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Using the pprof module, I could also extract some profiling information:
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```
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❯ go tool pprof sqlite-twitter tx
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File: sqlite-twitter
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Build ID: 708e90eba7948cb0851dfbf3bb6170ccaa418eff
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Type: cpu
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Time: Aug 24, 2018 at 4:26pm (CEST)
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Duration: 1.26mins, Total samples = 1.47mins (117.03%)
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Entering interactive mode (type "help" for commands, "o" for options)
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(pprof) cum
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(pprof) top20
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Showing nodes accounting for 65.26s, 73.82% of 88.40s total
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Dropped 222 nodes (cum <= 0.44s)
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Showing top 20 nodes out of 70
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flat flat% sum% cum cum%
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0.09s 0.1% 0.1% 58.96s 66.70% main.main
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0.44s 0.5% 0.6% 53.39s 60.40% database/sql.(*Tx).StmtContext
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0.11s 0.12% 0.72% 48.59s 54.97% database/sql.asString
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0.06s 0.068% 0.79% 47.55s 53.79% github.com/mattn/go-sqlite3.(*SQLiteConn).Prepare
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43.32s 49.00% 49.80% 44.27s 50.08% runtime.c128hash
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0.02s 0.023% 49.82% 40.16s 45.43% net/url.Values.Encode
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0.02s 0.023% 49.84% 39.79s 45.01% github.com/mattn/go-sqlite3.(*SQLiteConn).lastError.func3
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0.24s 0.27% 50.11% 23.36s 26.43% runtime.findrunnable
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0.03s 0.034% 50.15% 23.24s 26.29% runtime.casgstatus.func3
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0.44s 0.5% 50.64% 16.84s 19.05% runtime.forEachP
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0.17s 0.19% 50.84% 15.17s 17.16% internal/poll.runtime_pollSetDeadline
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14.98s 16.95% 67.78% 14.98s 16.95% runtime.duffcopy
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0.41s 0.46% 68.25% 10.67s 12.07% runtime.startm
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0.17s 0.19% 68.44% 8.62s 9.75% runtime.panicdottypeI
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0.11s 0.12% 68.56% 7.25s 8.20% runtime.needm
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0.31s 0.35% 68.91% 6.97s 7.88% runtime.panicdottypeE
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0.33s 0.37% 69.29% 6.90s 7.81% github.com/mattn/go-sqlite3.(*SQLiteDriver).Open
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1.81s 2.05% 71.33% 6.38s 7.22% runtime.newm1
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0.09s 0.1% 71.44% 4.75s 5.37% runtime.startlockedm
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2.11s 2.39% 73.82% 4.66s 5.27% runtime.schedtrace
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```
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### Raw statements and fmt.Sprintf
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Plain awful
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### Just one transaction
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```
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❯ time ./twitter-sqlite sample-twitter_rv.net
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./sqlite-twitter sample-twitter_rv.net 67.94s user 20.34s system 129% cpu 1:08.10 total
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```
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```
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❯ go tool pprof sqlite-twitter tx
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File: sqlite-twitter
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Build ID: 7ec752e835de12b94418fffb45515e1b0f89e89f
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Type: cpu
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Time: Aug 24, 2018 at 4:57pm (CEST)
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Duration: 1.25mins, Total samples = 1.46mins (117.52%)
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Entering interactive mode (type "help" for commands, "o" for options)
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(pprof) cum
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(pprof) top20
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Showing nodes accounting for 64.61s, 73.59% of 87.80s total
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Dropped 207 nodes (cum <= 0.44s)
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Showing top 20 nodes out of 70
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flat flat% sum% cum cum%
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0.08s 0.091% 0.091% 55.82s 63.58% main.updateStatus
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0.49s 0.56% 0.65% 51.68s 58.86% database/sql.(*Tx).StmtContext
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0.12s 0.14% 0.79% 46.56s 53.03% database/sql.asString
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0.11s 0.13% 0.91% 45.52s 51.85% github.com/mattn/go-sqlite3.(*SQLiteConn).Prepare
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41.12s 46.83% 47.74% 41.97s 47.80% runtime.c128hash
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0.01s 0.011% 47.76% 38.64s 44.01% github.com/mattn/go-sqlite3.(*SQLiteConn).lastError.func3
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0.05s 0.057% 47.81% 38.64s 44.01% net/url.Values.Encode
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0.38s 0.43% 48.25% 25.40s 28.93% runtime.findrunnable
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0.04s 0.046% 48.29% 25.25s 28.76% runtime.casgstatus.func3
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0.51s 0.58% 48.87% 17.72s 20.18% runtime.forEachP
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0.30s 0.34% 49.21% 15.93s 18.14% internal/poll.runtime_pollSetDeadline
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15.61s 17.78% 66.99% 15.61s 17.78% runtime.duffcopy
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0.46s 0.52% 67.52% 11.50s 13.10% runtime.startm
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0.15s 0.17% 67.69% 9.12s 10.39% runtime.panicdottypeI
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1.71s 1.95% 69.64% 7.39s 8.42% runtime.newm1
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0.06s 0.068% 69.70% 7.34s 8.36% runtime.needm
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0.36s 0.41% 70.11% 7.28s 8.29% runtime.panicdottypeE
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0.45s 0.51% 70.63% 5.93s 6.75% github.com/mattn/go-sqlite3.(*SQLiteDriver).Open
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2.48s 2.82% 73.45% 5.72s 6.51% runtime.schedtrace
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0.12s 0.14% 73.59% 4.77s 5.43% runtime.startlockedm
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```
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## CLI
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### Indexing first
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```
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❯ time sh sqlite.sh sample-twitter_rv.net
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sh sqlite.sh sample-twitter_rv.net 25.18s user 6.67s system 91% cpu 34.900 total
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```
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### Indexing afterwards
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```
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❯ time sh sqlite.sh sample-twitter_rv.net
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sh sqlite.sh sample-twitter_rv.net 14.91s user 1.30s system 84% cpu 19.279 total
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```
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# Comments
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There are way too many knobs to fiddle with, and I know very little about sqlite or SQL in general.
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This is a very specific use-case, and I've tried to tune the settings accordingly.
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Python was the easiest one to try.
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It is the language I'm more familiar with, and sqlite3 is included in the standard library, so only this file is needed.
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In Go, I tried compiling with `go build` in my machine and copying the binary to a remote host.
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I couldn't run it, apparently due to a mismatched glibc version or LDPATH.
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Instead, I had to use: `go build -ldflags "-linkmode external -extldflags -static" . `.
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It raises a warning, but I had no issue in my tests.
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In the end, the sqlite command line was the fastest of the three, and very easy to set up.
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If the file you are working with is sorted and without duplicates, the best option is to create the indexes after all the data has been loaded.
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You will also have to start over if the import fails or is interrupted.
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It is harder to remove duplicates afterwards in such a big dataset, and the cleanest solution is to simply copy all the unique entries to a new table and delete the old one. |