{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iquery --afl\n",
    "AFL% stream(\n",
    "    build(<x:int64 not null>[i=1:10:0:5], i),\n",
    "    'python -uc \"\n",
    "import scidbstrm\n",
    "import pyarrow\n",
    "import sys\n",
    "import tempfile\n",
    "\n",
    "def save(df):\n",
    "    rb = pyarrow.RecordBatch.from_pandas(df, preserve_index=False)\n",
    "\n",
    "    fn = tempfile.mkstemp()[1]\n",
    "    sys.stderr.write(\\\"{}\\\".format(fn))\n",
    "\n",
    "    with pyarrow.OSFile(fn, \\\"wb\\\") as sink:\n",
    "      writer = pyarrow.RecordBatchStreamWriter(sink, rb.schema)\n",
    "      writer.write_batch(rb)\n",
    "      writer.close()\n",
    "\n",
    "    return None\n",
    "\n",
    "scidbstrm.map(save)\"',\n",
    "    'format=feather',\n",
    "    'types=int64',\n",
    "    'names=unused');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 12\r\n",
      "-rw-------. 1 rares rares 3787 Feb 13 20:24 read-batches.ipynb\r\n",
      "-rw-------. 1 rares rares  556 Feb 13 19:37 tmpGqyn5p\r\n",
      "-rw-------. 1 rares rares  556 Feb 13 19:37 tmpzDxek1\r\n"
     ]
    }
   ],
   "source": [
    "!ls -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyarrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader = pyarrow.open_stream('tmpGqyn5p')\n",
    "table = reader.read_all()\n",
    "table.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyarrow.lib.Column object at 0x7fe5281ce360>\n",
       "chunk 0: <pyarrow.lib.Int64Array object at 0x7fe5281c2c28>\n",
       "[\n",
       "  6,\n",
       "  7,\n",
       "  8,\n",
       "  9,\n",
       "  10\n",
       "]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader = pyarrow.open_stream('tmpzDxek1')\n",
    "table = reader.read_all()\n",
    "table.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pyarrow.lib.Column object at 0x7fe5281ce450>\n",
       "chunk 0: <pyarrow.lib.Int64Array object at 0x7fe5281dc598>\n",
       "[\n",
       "  1,\n",
       "  2,\n",
       "  3,\n",
       "  4,\n",
       "  5\n",
       "]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table[0]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}