deletions | additions       diff --git a/data/.ipynb_checkpoints/Test-checkpoint.ipynb b/data/.ipynb_checkpoints/Test-checkpoint.ipynb  new file mode 100644  index 0000000..b72ecb8  --- /dev/null  +++ b/data/.ipynb_checkpoints/Test-checkpoint.ipynb 
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{  "metadata": {  "name": "",  "signature": "sha256:1e1e421f25e20b992377ea9e7d76096b35c2a78c6ec76c2034a0c83df507f564"  },  "nbformat": 3,  "nbformat_minor": 0,  "worksheets": [  {  "cells": [  {  "cell_type": "code",  "collapsed": false,  "input": [  "%matplotlib inline\n",  "import seaborn as sns\n",  "import numpy as np\n",  "from math import pi"  ],  "language": "python",  "metadata": {},  "outputs": [],  "prompt_number": 6  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "sns.set_style(\"whitegrid\")\n",  "sns.despine()"  ],  "language": "python",  "metadata": {},  "outputs": [  {  "metadata": {},  "output_type": "display_data",  "text": [  ""  ]  }  ],  "prompt_number": 7  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "df = sns.load_dataset(\"anscombe\")"  ],  "language": "python",  "metadata": {},  "outputs": [],  "prompt_number": 8  },  {  "cell_type": "code",  "collapsed": false,  "input": [],  "language": "python",  "metadata": {},  "outputs": []  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "sns.lmplot(\"x\", \"y\", col=\"dataset\", hue=\"dataset\", data=df,\n",  " col_wrap=2, ci=None, palette=\"muted\", size=4,\n",  " scatter_kws={\"s\": 50, \"alpha\": 1})"  ],  "language": "python",  "metadata": {},  "outputs": [  {  "metadata": {},  "output_type": "pyout",  "prompt_number": 3,  "text": [  ""  ]  },  {  "metadata": {},  "output_type": "display_data",  "png": 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JTzcJnuzqa8WVGaoZ/r0lAyeu1FYzdInU4/3pbSFzZTUDkbXszN6On3L3QisY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\nUUTdjz/COSgIwXPmWHZxRGRzBL2An78vwvUfOqoZeuLiwV8TZL94TY6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"text": [  ""  ]  }  ],  "prompt_number": 3  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "import mysql.connector as sql"  ],  "language": "python",  "metadata": {},  "outputs": [  {  "ename": "ImportError",  "evalue": "No module named mysql.connector",  "output_type": "pyerr",  "traceback": [  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",  "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmysql\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnector\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msql\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",  "\u001b[0;31mImportError\u001b[0m: No module named mysql.connector"  ]  }  ],  "prompt_number": 4  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "import yaml, glob\n",  "import pandas as pd"  ],  "language": "python",  "metadata": {},  "outputs": [],  "prompt_number": 38  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "data = []\n",  "for path in glob.glob(\"/Users/bholt/hub/tapir/src/data/*.yml\"):\n",  " with open(path, 'r') as f:\n",  " data.append(yaml.load(f))"  ],  "language": "python",  "metadata": {},  "outputs": [],  "prompt_number": 36  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "data"  ],  "language": "python",  "metadata": {},  "outputs": [  {  "metadata": {},  "output_type": "pyout",  "prompt_number": 37,  "text": [  "[{'alpha': 0.6,\n",  " 'client': 'dsretwis',\n",  " 'clientargs': '-n 1000 -t 1',\n",  " 'closest': -1,\n",  " 'config': ['/Users/bholt/hub/tapir/src/tools/shard',\n",  " '/Users/bholt/hub/tapir/src/tools/shard'],\n",  " 'debug': 0,\n",  " 'duration': 5,\n",  " 'edgefactor': 8,\n",  " 'err': 0,\n",  " 'error': 0,\n",  " 'filename': '/Users/bholt/hub/tapir/src/data/claret.dUrrkO.yml',\n",  " 'gen': 'adhoc',\n",  " 'genargs': '--initposts=100 --initusers=50',\n",  " 'initposts': 20,\n",  " 'initusers': 10,\n",  " 'keys': 100000,\n",  " 'length': 1,\n",  " 'load': ['/Users/bholt/hub/tapir/src/tools/generatedKeys',\n",  " '/Users/bholt/hub/tapir/src/tools/generatedKeys'],\n",  " 'mode': ['ds-l', 'ds-l'],\n",  " 'name': 'claret',\n",  " 'nclient': 2,\n",  " 'nkeys': 100000,\n",  " 'nshard': 1,\n",  " 'retwis_follow_success': 0.592715,\n",  " 'retwis_newuser_success': 0.53125,\n",  " 'retwis_post_success': 0.656327,\n",  " 'retwis_timeline_success': 0.564626,\n",  " 'rtime': 5,\n",  " 'shards': 1,\n",  " 'skew': 0,\n",  " 'store': 'dsstore',\n",  " 'threads': 1,\n",  " 'tlen': 1,\n",  " 'total_time': 5.50877,\n",  " 'txn_count': 635.5,\n",  " 'txn_failed': 350.5,\n",  " 'txn_retries': 1813,\n",  " 'txn_time': 4.84044,\n",  " 'txns': 1000,\n",  " 'wper': 0,\n",  " 'writes': 0,\n",  " 'zalpha': 0.6},\n",  " {'alpha': 0.6,\n",  " 'client': 'dsretwis',\n",  " 'clientargs': '-n 1000 -t 1',\n",  " 'closest': -1,\n",  " 'config': ['/Users/bholt/hub/tapir/src/tools/shard',\n",  " '/Users/bholt/hub/tapir/src/tools/shard',\n",  " '/Users/bholt/hub/tapir/src/tools/shard'],\n",  " 'debug': 0,\n",  " 'duration': 5,\n",  " 'edgefactor': 8,\n",  " 'err': 0,\n",  " 'error': 0,\n",  " 'filename': '/Users/bholt/hub/tapir/src/data/claret.E5YjyF.yml',\n",  " 'gen': 'adhoc',\n",  " 'genargs': '--initposts=100 --initusers=50',\n",  " 'initposts': 20,\n",  " 'initusers': 10,\n",  " 'keys': 100000,\n",  " 'length': 1,\n",  " 'load': ['/Users/bholt/hub/tapir/src/tools/generatedKeys',\n",  " '/Users/bholt/hub/tapir/src/tools/generatedKeys',\n",  " '/Users/bholt/hub/tapir/src/tools/generatedKeys'],\n",  " 'mode': ['ds-l', 'ds-l', 'ds-l'],\n",  " 'name': 'claret',\n",  " 'nclient': 3,\n",  " 'nkeys': 100000,\n",  " 'nshard': 1,\n",  " 'retwis_follow_success': 0.958057,\n",  " 'retwis_newuser_success': 0.0625001,\n",  " 'retwis_post_success': 0.995864,\n",  " 'retwis_timeline_success': 0.994331,\n",  " 'rtime': 5,\n",  " 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'edgefactor': 8,\n",  " 'err': 0,\n",  " 'error': 0,\n",  " 'filename': '/Users/bholt/hub/tapir/src/data/claret.VmDRWd.yml',\n",  " 'gen': 'adhoc',\n",  " 'genargs': '--initposts=100 --initusers=50',\n",  " 'initposts': 20,\n",  " 'initusers': 10,\n",  " 'keys': 100000,\n",  " 'length': 1,\n",  " 'load': ['/Users/bholt/hub/tapir/src/tools/generatedKeys'],\n",  " 'mode': ['ds-l'],\n",  " 'name': 'claret',\n",  " 'nclient': 1,\n",  " 'nkeys': 100000,\n",  " 'nshard': 1,\n",  " 'retwis_follow_success': 1,\n",  " 'retwis_newuser_success': 1,\n",  " 'retwis_post_success': 1,\n",  " 'retwis_timeline_success': 1,\n",  " 'rtime': 5,\n",  " 'shards': 1,\n",  " 'skew': 0,\n",  " 'store': 'dsstore',\n",  " 'threads': 1,\n",  " 'tlen': 1,\n",  " 'total_time': 4.13925,\n",  " 'txn_count': 1047,\n",  " 'txn_failed': 0,\n",  " 'txn_retries': 0,\n",  " 'txn_time': 4.13626,\n",  " 'txns': 1000,\n",  " 'wper': 0,\n",  " 'writes': 0,\n",  " 'zalpha': 0.6}]"  ]  }  ],  "prompt_number": 37  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "df = pd.DataFrame(data)"  ],  "language": "python",  "metadata": {},  "outputs": [],  "prompt_number": 40  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "df['txn_abort_rate'] = df['txn_failed'] / (df['txn_count'] + df['txn_failed'])"  ],  "language": "python",  "metadata": {},  "outputs": [],  "prompt_number": 46  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "sns.barplot(data=df, x='nclient', y='total_time')"  ],  "language": "python",  "metadata": {},  "outputs": [  {  "metadata": {},  "output_type": "pyout",  "prompt_number": 42,  "text": [  ""  ]  },  {  "metadata": {},  "output_type": "display_data",  "png": 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"text": [  ""  ]  }  ],  "prompt_number": 47  },  {  "cell_type": "code",  "collapsed": false,  "input": [],  "language": "python",  "metadata": {},  "outputs": []  }  ],  "metadata": {}  }  ]  }  diff --git a/data/Test.ipynb b/data/Test.ipynb  new file mode 100644  index 0000000..53ff8d7  --- /dev/null  +++ b/data/Test.ipynb 
...
{  "metadata": {  "name": "",  "signature": "sha256:67fe8ac608a4b17cd3f100b09d8369e9358618007af770d2b58a69a61133355e"  },  "nbformat": 3,  "nbformat_minor": 0,  "worksheets": [  {  "cells": [  {  "cell_type": "code",  "collapsed": false,  "input": [  "%matplotlib inline\n",  "import seaborn as sns\n",  "import numpy as np\n",  "import yaml, glob\n",  "import pandas as pd\n",  "import dataset"  ],  "language": "python",  "metadata": {},  "outputs": [],  "prompt_number": 61  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "sns.set_style(\"whitegrid\")\n",  "sns.despine()"  ],  "language": "python",  "metadata": {},  "outputs": [  {  "metadata": {},  "output_type": "display_data",  "text": [  ""  ]  }  ],  "prompt_number": 7  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "db = dataset.connect('mysql://[email protected]/claret')\n",  "tbl = db['tapir']"  ],  "language": "python",  "metadata": {},  "outputs": [],  "prompt_number": 75  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "df = pd.DataFrame([dict(row) for row in tbl.all()])"  ],  "language": "python",  "metadata": {},  "outputs": [],  "prompt_number": 76  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "#data = []\n",  "#for path in glob.glob(\"/Users/bholt/hub/tapir/src/data/*.yml\"):\n",  "# with open(path, 'r') as f:\n",  "# data.append(yaml.load(f))"  ],  "language": "python",  "metadata": {},  "outputs": [],  "prompt_number": 73  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "df['txn_abort_rate'] = df['txn_failed'] / (df['txn_count'] + df['txn_failed'])\n",  "df['throughput'] = df['txn_count'] * df['nclient'] / df['total_time']"  ],  "language": "python",  "metadata": {},  "outputs": [],  "prompt_number": 69  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "sns.barplot(data=df, x='nclient', y='throughput')"  ],  "language": "python",  "metadata": {},  "outputs": [  {  "metadata": {},  "output_type": "pyout",  "prompt_number": 70,  "text": [  ""  ]  },  {  "metadata": {},  "output_type": "display_data",  "png": 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"text": [  ""  ]  }  ],  "prompt_number": 72  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "import dataset"  ],  "language": "python",  "metadata": {},  "outputs": [],  "prompt_number": 60  },  {  "cell_type": "code",  "collapsed": false,  "input": [  "db = dataset.connect()"  ],  "language": "python",  "metadata": {},  "outputs": []  }  ],  "metadata": {}  }  ]  }  diff --git a/data/freezer_config.yaml b/data/freezer_config.yaml  new file mode 100644  index 0000000..583c4d6  --- /dev/null  +++ b/data/freezer_config.yaml 
...
common:  database: mysql:///claret?read_default_file=/Users/bholt/.my.cnf  prefix: .  format: csv  exports:  - query: SELECT * FROM tapir  filename: tapir.csv  diff --git a/data/tapir.csv b/data/tapir.csv  new file mode 100644  index 0000000..aac9599  --- /dev/null  +++ b/data/tapir.csv 
...
id,load,retwis_post_success,initusers,duration,nclient,txn_failed,retwis_newuser_success,skew,initposts,filename,zalpha,nkeys,edgefactor,config,genargs,mode,total_time,clientargs,retwis_timeline_success,closest,txn_count,keys,gen,debug,threads,alpha,retwis_follow_success,txn_time,rtime,name,err,shards,txns,length,client,store,error,wper,txn_retries,tlen,nshard,writes  1,"/Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys",0.983871,10,5,4,70.25,0.03125,0,20,/Users/bholt/hub/tapir/src/data/claret.MiE9tC.yml,0.6,100000,8,"/Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard",--initposts=100 --initusers=50,"ds-l, ds-l, ds-l, ds-l",17.5121,-n 1000 -t 1,0.97534,-1,975.75,100000,adhoc,0,1,0.6,0.933775,17.1041,5,claret,0,1,1000,1,dsretwis,dsstore,0,0,569.5,1,1,0  2,"/Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys",0.995864,10,5,3,54.6667,0.0625001,0,20,/Users/bholt/hub/tapir/src/data/claret.E5YjyF.yml,0.6,100000,8,"/Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard",--initposts=100 --initusers=50,"ds-l, ds-l, ds-l",13.7979,-n 1000 -t 1,0.994331,-1,990.667,100000,adhoc,0,1,0.6,0.958057,13.4636,5,claret,0,1,1000,1,dsretwis,dsstore,0,0,377.0,1,1,0  3,/Users/bholt/hub/tapir/src/tools/generatedKeys,1.0,10,5,1,0.0,1.0,0,20,/Users/bholt/hub/tapir/src/data/claret.HSFOkp.yml,0.6,100000,8,/Users/bholt/hub/tapir/src/tools/shard,--initposts=100 --initusers=50,ds-l,4.77055,-n 1000 -t 1,1.0,-1,1047.0,100000,adhoc,0,1,0.6,1.0,4.76684,5,claret,0,1,1000,1,dsretwis,dsstore,0,0,0.0,1,1,0  4,"/Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys",,10,5,2,,,0,20,/Users/bholt/hub/tapir/src/data/claret.JQ39XH.yml,0.6,100000,8,"/Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard",--initposts=100 --initusers=50,"ds-l, ds-l",,-n 1000 -t 1,,-1,,100000,adhoc,0,1,0.6,,,5,claret,0,1,1000,1,dsretwis,dsstore,0,0,,1,1,0  5,"/Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys",0.983871,10,5,4,70.25,0.03125,0,20,/Users/bholt/hub/tapir/src/data/claret.MiE9tC.yml,0.6,100000,8,"/Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard",--initposts=100 --initusers=50,"ds-l, ds-l, ds-l, ds-l",17.5121,-n 1000 -t 1,0.97534,-1,975.75,100000,adhoc,0,1,0.6,0.933775,17.1041,5,claret,0,1,1000,1,dsretwis,dsstore,0,0,569.5,1,1,0  6,"/Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys",0.99938,10,5,4,71.25,0.0104166,0,20,/Users/bholt/hub/tapir/src/data/claret.QZdrZS.yml,0.6,100000,8,"/Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard",--initposts=100 --initusers=50,"ds-l, ds-l, ds-l, ds-l",20.0689,-n 1000 -t 1,0.978741,-1,975.75,100000,adhoc,0,1,0.6,0.885762,19.5894,5,claret,0,1,1000,1,dsretwis,dsstore,0,0,560.0,1,1,0  7,/Users/bholt/hub/tapir/src/tools/generatedKeys,1.0,10,5,1,0.0,1.0,0,20,/Users/bholt/hub/tapir/src/data/claret.VmDRWd.yml,0.6,100000,8,/Users/bholt/hub/tapir/src/tools/shard,--initposts=100 --initusers=50,ds-l,4.13925,-n 1000 -t 1,1.0,-1,1047.0,100000,adhoc,0,1,0.6,1.0,4.13626,5,claret,0,1,1000,1,dsretwis,dsstore,0,0,0.0,1,1,0  8,"/Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys",0.656327,10,5,2,350.5,0.53125,0,20,/Users/bholt/hub/tapir/src/data/claret.dUrrkO.yml,0.6,100000,8,"/Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard",--initposts=100 --initusers=50,"ds-l, ds-l",5.50877,-n 1000 -t 1,0.564626,-1,635.5,100000,adhoc,0,1,0.6,0.592715,4.84044,5,claret,0,1,1000,1,dsretwis,dsstore,0,0,1813.0,1,1,0  9,"/Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys",,10,5,3,,,0,20,/Users/bholt/hub/tapir/src/data/claret.ifpUb2.yml,0.6,100000,8,"/Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard",--initposts=100 --initusers=50,"ds-l, ds-l, ds-l",,-n 1000 -t 1,,-1,,100000,adhoc,0,1,0.6,,,5,claret,0,1,1000,1,dsretwis,dsstore,0,0,,1,1,0  10,"/Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys",0.833747,10,5,2,196.0,0.677083,0,20,/Users/bholt/hub/tapir/src/data/claret.jriIM0.yml,0.6,100000,8,"/Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard",--initposts=100 --initusers=50,"ds-l, ds-l",5.08756,-n 1000 -t 1,0.758503,-1,811.0,100000,adhoc,0,1,0.6,0.718543,4.7575,5,claret,0,1,1000,1,dsretwis,dsstore,0,0,1051.5,1,1,0  11,"/Users/bholt/hub/tapir/src/tools/generatedKeys, /Users/bholt/hub/tapir/src/tools/generatedKeys",,10,5,2,,,0,20,/Users/bholt/hub/tapir/src/data/claret.vFkUcI.yml,0.6,100000,8,"/Users/bholt/hub/tapir/src/tools/shard, /Users/bholt/hub/tapir/src/tools/shard",--initposts=100 --initusers=50,"ds-l, ds-l",,-n 1000 -t 1,,-1,,100000,adhoc,0,1,0.6,,,5,claret,0,1,1000,1,dsretwis,dsstore,0,0,,1,1,0