From 4e72a5da450dc6b45ca0c16d40379b94b6731494 Mon Sep 17 00:00:00 2001 From: Adrian Vogelsgesang Date: Mon, 22 Jan 2024 20:37:47 +0100 Subject: [PATCH] Update README --- README.md | 30 +++++++++--------- media/sample_graph_complex.png | Bin 160305 -> 0 bytes media/sample_graph_simple.png | Bin 165349 -> 0 bytes media/tpch-q19.png | Bin 0 -> 73882 bytes media/tpch-q2.png | Bin 0 -> 193131 bytes .../webpack/webpack-create-examples-list.ts | 2 +- 6 files changed, 16 insertions(+), 16 deletions(-) delete mode 100644 media/sample_graph_complex.png delete mode 100644 media/sample_graph_simple.png create mode 100644 media/tpch-q19.png create mode 100644 media/tpch-q2.png diff --git a/README.md b/README.md index 57da5892..3887fb45 100644 --- a/README.md +++ b/README.md @@ -4,8 +4,8 @@ Query Graphs Helping people see and understand queries - [Visualize your own query!](http://tableau.github.io/query-graphs/index.html) -[Sample Visualization](https://tableau.github.io/query-graphs/index.html?file=examples%2Fpostgres%2Fbitmapscan.json&title=bitmapscan.json) -[Sample Visualization](http://tableau.github.io/query-graphs/index.html?file=examples%2Fpostgres%2Flearning_to_optimize_federated_queries%2Fjob%2Funfederated%2Fdisable_nestloop%2F10a_dting-linux.json&title=10a_dting-linux.json) +[Sample Visualization](https://tableau.github.io/query-graphs/index.html?file=examples%2Fhyper%2Ftpch%2Ftpch-q19-analyze.plan.json&title=tpch-q19-analyze.plan.json) +[Sample Visualization](https://tableau.github.io/query-graphs/index.html?file=examples%2Fhyper%2Ftpch%2Ftpch-q2-analyze.plan.json&title=tpch-q2-analyze.plan.json) Description ----------- @@ -16,16 +16,22 @@ Query graphs is based on the experience of Tableau and the Hyper team with debug In contrast to many competing plan visualizers, query graphs: * visualizes large and complex query plans without presenting an overwhelming amount of information, * immediately draws your attention to tne most critical parts of the query plan through proper color coding, -* allows you to drill down on the interesting parts of the query-plan, e.g., by hovering over individual nodes, +* allows you to drill down on the interesting parts of the query-plan, e.g., by expanding more details by clicking on the node of interest, * supports multiple different plan types (currently Postgres, Hyper and PostgresQL), * can be easily embeded into larger tools, -* works offline. +* works fully offline and can be installed as a "web app" from your browser. For a first impression see our [list of example visualizations](https://tableau.github.io/query-graphs/examples.html), a few particularly examples are: -* [PostgreSQL query plan](https://tableau.github.io/query-graphs/index.html?file=examples%2Fpostgres%2Flearning_to_optimize_federated_queries%2Fjob%2Ffederated%2Fdisable_nestloop%2F10a_ricole-lx.json&title=10a_ricole-lx.json) -* [A complex PostgreSQL query plan](https://tableau.github.io/query-graphs/index.html?file=examples%2Fpostgres%2Flearning_to_optimize_federated_queries%2Fjob%2Funfederated%2Fdisable_nestloop%2F10a_dting-linux.json&title=10a_dting-linux.json) -* [Hyper query plan for TPC-H Q1](https://tableau.github.io/query-graphs/index.html?file=examples%2Fhyper%2Ftpch-q1.plan.json&title=tpch-q1.plan.json) -* [Optimizer steps of Hyper for TPC-H Q2](https://tableau.github.io/query-graphs/index.html?file=examples%2Fhyper%2Ftpch-q2-steps.plan.json&title=tpch-q2-steps.plan.json) +* [PostgreSQL TPC-H Query 2](https://tableau.github.io/query-graphs/index.html?file=examples%2Fpostgres%2Ftpch%2Ftpch-q2-analyze.plan.json&title=tpch-q2-analyze.plan.json) +* [Hyper TPC-H Query 2](https://tableau.github.io/query-graphs/index.html?file=examples%2Fhyper%2Ftpch%2Ftpch-q2-analyze.plan.json&title=tpch-q2-analyze.plan.json) +* [Optimizer steps of Hyper for TPC-H Q2](https://tableau.github.io/query-graphs/index.html?file=examples%2Fhyper%2Ftpch%2Ftpch-q2-steps.plan.json&title=tpch-q2-steps.plan.json) + + +Installation +------------ + +You can use https://tableau.github.io/query-graphs/, you do not need to install anything. +If you still need a local deployment, see the [Developer instructions](DEVELOPMENT.md) for instructions. Usage ----- @@ -43,12 +49,6 @@ Supported query plan formats: * Hyper's query plans. You can run Hyper (the database system powering Tableau) locally through HyperAPI, and send Hyper `EXPLAIN (VERBOSE)` queries to obtain query plans. See [dump-plans.py](plan-dumper/dump-plans.py) for an example. * Tableau logical queries. Can be obtained from the log files of Tableau Desktop or Tableau Online. -Installation ------------- - -You can use https://tableau.github.io/query-graphs/, you do not need to install anything. -If you still need a local deployment, see the [Developer instructions](DEVELOPMENT.md) for instructions. - Contributing ------------ @@ -63,4 +63,4 @@ Acknowledgements ---------------- The dragging, zooming, panning, collapsing functionality originated from -“D3.js Drag and Drop, Zoomable, Panning, Collapsible Tree with Auto-Sizing” (Rob Schmuecker’s block #7880033). \ No newline at end of file +“D3.js Drag and Drop, Zoomable, Panning, Collapsible Tree with Auto-Sizing” (Rob Schmuecker’s block #7880033). diff --git a/media/sample_graph_complex.png b/media/sample_graph_complex.png deleted file mode 100644 index 1c5e0d403ba9807b3d944517a8e97c7bed3d4245..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 160305 zcmeFZWmHzt7B-3qND9&+B_fh9h;)h|B_SY4mvnc7h=53mNJy(74blxFrKEI&ba(e% zTY16noHOpfdw<+<8H2&gX79b$nrp^0pE(x+^0MMs=%nZf2nbk`5~2zS2*`fmn+g>L zT-jFi@kcS@8*ZxjZrb=yp7e=n;)~75NC@|P z%(%@RwZ{vc#qluPwIC!7wnRwSK4C`KtVixDUDuf{eXn$9fkgNU!TiIb>CNZ3%I421UEE#itYv!TqY-EoOulX}_6M@g?w` zuh*9=L{rsg5^zfgIMG}C;c{IcxLuz|U5);NG@>QK@JiAK!e*<87UA~i4H7*fHtpLe z!gk#CSNN%;u=8&1O}My(6O`W6<1>bN6VG0)WUt=(u)}pm^6<25B#SyL%S^aOMD(5{ zi__5AV9Xh_OW9^EF{gaGh>2+2C(nU8KAvzIf@pM`C~g|wN1bM`-Nf7t#;*v|i@1n> z>Z0J7{W&2Bkj6^bAv^=(Y-N}*py99}0^ypKrxkmcbtSKW ziB3?#fIUOvM+ik>So0MAf%=1+bU6Ok7*F(lKXiTNj_{C7F#oD$9w=WQpgeIE`THwW z1Fx^${HwyJUkR5pgj=O|303riB@g|0?+9;p9-%zMupiRrOe|!EG zOO#pDL32II_70}CH~#Z0SF9D-d>G$dGpt`H#eVaR5(n*txv}Q@w=4vFk<%8O%t~JG zR`0>@-UK|qN$OpwaHqeIZ+btS{AeusoBp}z+3R-SW}kyuk*BR^Zk}&X8oyHErRYuj z-1rdV6Im~>hsySn_)%d~@H)Z>cIeA~sORN*k^j_%vHbgS?cO-U z{_42=V3_8QyQ+ydMT?JYQv7`Un%)wL<<_i)-s=zg=vm-z=o!>;nsemN{pi#fMp6#dYz(1a|F!DM`?Uh)a|70N_|=owr#C_~-=xncx26oGbk7vn z<_NPE3ENEN(Mf-i6jS|s_9#yp#Y_;bQODfg?^?xK;JQB+K0UsdprMvFZQbCDkcb9!DN_IX<_GUq zajCF~AFj_C6?~81+>1MVc@{bIUL z$t*&YB;9FSh&$@j_ld4kdS0dw%fok92SKrkIGShnm5aVP6N+@hJvMqy4942#uTohk zW-rdR9I~*Mz6(x@#)Yuk(3Seotc%9^&rKDq)9$;v6KMq|Hs(%+Zde{OdOu>l!;R~X zcJn*82%#?e$7uOS0}r?#b6;Icx&GblR@e`!A>69pRna1?cVy{Us4Z`=(s15T4_K+E zzzujVVSD=^Tu(wn;YxaRVMw9B{Y(4co(GFh6;;?3?Ve&NNDB}bq+%_CF34bjc(^`$-ctA=O(f}OkGUf{An&?1=4|82A>=HCQaMA zGJf%Xa!}6{kIfNK;CsX;=N7PQxq>kSvzOrI8!t20IoDm% z-SLahKA^m~{N||nsKRJfIdl1_E3fOStE8*q4#RfHLJ=FQAC^zu4+AFsn>r(U?p<7+ zMhh6*L`(hi+TCY5tgTc(gmvWgzIOcnQcL&ni*A~3$OL*FZ=L_6(lDRSXP@aG#Rjz# zJ79H_&vFHEWj!D8*4+zwqJM3QdupL^I0#0z7abk*Dpulg--oz+3-=77Q9c;P=5PvF z4w*dS!eGVwkj=@byZbJ^={cL-hnd#yR!ckmEmXU*1LZBkEkO+RK+!-K!XqXMH3g?AEgU*x%vUU!3$@HV+7Eq#vt{zzL}GbkiHcE*Zw}uYHXXj^ zc)vop!o5POf^1jR&FbjpQShz`x%@TkYabu_))9#Jbap?PF73>7;MOheu-;g2o+yb= zlVwU3Ni|JvYR89WriNL-g5P9PN<>W$&uhM90qf;yB?q zObP4(++T#o6hE-CFx_#Vlhk7m-Pn#Z=x8=v(XG1fh!%-LdHwX;u0h9hE?*t$32Itu z=8wD|13oT2r1VvNW#IeN-@)JQl^nL!8!A!JM_I2++t?=7xu3?Zc4~b#3K1li3=Rs_ zpgg$UOf5y)%ZftI?%JBchW#n~b3>;C8?patP0aonu={YuksTVg4u=#<>#?As5d&#BgLg!ooBBQ$>fo zM|p+&yCOBdu|yL<$<+uen6*mw&CqJ5_0pwHR2SxGD`|h7ql%ffG>T1l zY|5MuvI)Ey9kYA1R15PxdZV8fWq!z%Rz52-?25rKST%_4SLkc*FOCq8=+7-sD{&v4 z+xfo3#rTqO*MfEIt8<-y@NsZIC$dGxpm~0o{Z=aTCuXN&KdbEQ!gQwLM*ww@A8i^? 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generateSubDirList(dirPath: string, createLink: CreateLink): Prom } async function generateExamplesList(dirPath: string, createLink: CreateLink) { - let html = "Favorites

Favorites

    "; + let html = "Example Query Plans

    Example Query Plans

      "; html += await generateSubDirList(dirPath, createLink); html += "
    "; return html;