templates/spec-api-sql/README.ipynb

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            "source": [
                "To run this locally, [install Ploomber](https://docs.ploomber.io/en/latest/get-started/quick-start.html) and execute: `ploomber examples -n templates/spec-api-sql`\n",
                "\n",
                "[![binder-logo](https://raw.githubusercontent.com/ploomber/projects/master/_static/open-in-jupyterlab.svg)](https://binder.ploomber.io/v2/gh/ploomber/binder-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Fploomber%252Fprojects%26urlpath%3Dlab%252Ftree%252Fprojects%252Ftemplates/spec-api-sql%252FREADME.ipynb%26branch%3Dmaster)\n",
                "\n",
                "Found an issue? [Let us know.](https://github.com/ploomber/projects/issues/new?title=templates/spec-api-sql%20issue)\n",
                "\n",
                "Questions? [Ask us on Slack.](https://ploomber.io/community/)\n",
                "\n"
            ]
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            "source": [
                "# SQL/Python pipeline\n",
                "\n",
                "<!-- start description -->\n",
                "Use SQL scripts to manipulate data in a database, dump a table, and plot it with Python.\n",
                "<!-- end description -->"
            ]
        },
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                "tags": []
            },
            "source": [
                "## Create sample data\n"
            ]
        },
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            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Requirement already satisfied: sqlalchemy in /Users/Edu/miniconda3/envs/projects/lib/python3.9/site-packages (1.4.25)\n",
                        "Requirement already satisfied: faker in /Users/Edu/miniconda3/envs/projects/lib/python3.9/site-packages (9.2.0)\n",
                        "Requirement already satisfied: numpy in /Users/Edu/miniconda3/envs/projects/lib/python3.9/site-packages (1.21.2)\n",
                        "Requirement already satisfied: pandas in /Users/Edu/miniconda3/envs/projects/lib/python3.9/site-packages (1.3.3)\n",
                        "Requirement already satisfied: greenlet!=0.4.17 in /Users/Edu/miniconda3/envs/projects/lib/python3.9/site-packages (from sqlalchemy) (1.1.2)\n",
                        "Requirement already satisfied: text-unidecode==1.3 in /Users/Edu/miniconda3/envs/projects/lib/python3.9/site-packages (from faker) (1.3)\n",
                        "Requirement already satisfied: python-dateutil>=2.4 in /Users/Edu/miniconda3/envs/projects/lib/python3.9/site-packages (from faker) (2.8.2)\n",
                        "Requirement already satisfied: pytz>=2017.3 in /Users/Edu/miniconda3/envs/projects/lib/python3.9/site-packages (from pandas) (2021.3)\n",
                        "Requirement already satisfied: six>=1.5 in /Users/Edu/miniconda3/envs/projects/lib/python3.9/site-packages (from python-dateutil>=2.4->faker) (1.16.0)\n"
                    ]
                }
            ],
            "source": [
                "%%bash\n",
                "# create sample data\n",
                "cd setup\n",
                "bash setup.sh\n",
                "# move back to the original spec-api-sql folder\n",
                "cd .."
            ]
        },
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            },
            "source": [
                "## Definition"
            ]
        },
        {
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            "source": [
                "```yaml\n",
                "# Content of pipeline.yaml\n",
                "meta:\n",
                "  product_default_class:\n",
                "    SQLScript: SQLiteRelation\n",
                "\n",
                "  jupyter_hot_reload: True\n",
                "\n",
                "config:\n",
                "  hot_reload: True\n",
                "\n",
                "clients:\n",
                "  # tasks\n",
                "  SQLScript: config.get_client\n",
                "  SQLDump: config.get_client\n",
                "  # products\n",
                "  SQLiteRelation: config.get_client\n",
                "\n",
                "tasks:\n",
                "  - source: filter_sales.sql\n",
                "    product: [filtered_sales, table]\n",
                "    name: filter_sales\n",
                "\n",
                "  - source: group_sales.sql\n",
                "    product: [grouped_sales, table]\n",
                "    name: group_sales\n",
                "\n",
                "  - source: filter_prices.sql\n",
                "    product: [filtered_prices, table]\n",
                "    name: filter_prices\n",
                "\n",
                "  - source: join.sql\n",
                "    product: [joined, table]\n",
                "    name: join\n",
                "\n",
                "  - class: SQLDump\n",
                "    source: join_dump.sql\n",
                "    product: output/joined_data.csv\n",
                "    name: join_dump\n",
                "    chunksize: null\n",
                "\n",
                "  - source: plot.py\n",
                "    product: output/plot.html\n",
                "```"
            ]
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            "source": [
                "The first two sections configure our pipeline; the `tasks` section is the\n",
                "actual pipeline definition. First, we see that we have a few SQL transformations,\n",
                "then we dump a table into a CSV file and produce an HTML report at the end.\n",
                "The order here doesn't matter, the source code itself declares its upstream dependencies, and Ploomber extracts them to execute your pipeline."
            ]
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            "id": "947e4434",
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                "tags": []
            },
            "source": [
                "## Plot"
            ]
        },
        {
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            "id": "bf09fcec",
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                "execution": {
                    "iopub.execute_input": "2022-05-14T00:02:12.814895Z",
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            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Loading pipeline...\n",
                        "Plot saved at: pipeline.png\n"
                    ]
                }
            ],
            "source": [
                "%%bash\n",
                "ploomber plot"
            ]
        },
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            "source": [
                "If running in Jupyter, load the plot with this code:"
            ]
        },
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\n",
                        "text/plain": [
                            "<IPython.core.display.Image object>"
                        ]
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "from IPython.display import Image\n",
                "Image(filename='pipeline.png')\n",
                "\n",
                "# NOTE: ploomber plot will generate a pipeline.html (not .png) file if\n",
                "# pygraphviz is missing. In such case, open the file to view the pipeline plot"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "fa5fd2c4",
            "metadata": {
                "papermill": {
                    "duration": 0.030202,
                    "end_time": "2022-05-14T00:02:18.657630",
                    "exception": false,
                    "start_time": "2022-05-14T00:02:18.627428",
                    "status": "completed"
                },
                "tags": []
            },
            "source": [
                "Otherwise, open the `pipeline.png` file directly."
            ]
        },
        {
            "cell_type": "markdown",
            "id": "6969ad66",
            "metadata": {
                "papermill": {
                    "duration": 0.027389,
                    "end_time": "2022-05-14T00:02:18.714148",
                    "exception": false,
                    "start_time": "2022-05-14T00:02:18.686759",
                    "status": "completed"
                },
                "tags": []
            },
            "source": [
                "## Build"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "id": "ffbe9fae",
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2022-05-14T00:02:18.778268Z",
                    "iopub.status.busy": "2022-05-14T00:02:18.777569Z",
                    "iopub.status.idle": "2022-05-14T00:02:28.471170Z",
                    "shell.execute_reply": "2022-05-14T00:02:28.471663Z"
                },
                "papermill": {
                    "duration": 9.727574,
                    "end_time": "2022-05-14T00:02:28.471881",
                    "exception": false,
                    "start_time": "2022-05-14T00:02:18.744307",
                    "status": "completed"
                },
                "tags": []
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Loading pipeline...\n",
                        "name           Ran?      Elapsed (s)    Percentage\n",
                        "-------------  ------  -------------  ------------\n",
                        "filter_sales   True         0.005482     0.15679\n",
                        "filter_prices  True         0.001386     0.0396408\n",
                        "group_sales    True         0.001327     0.0379533\n",
                        "join           True         0.001258     0.0359799\n",
                        "join_dump      True         0.002534     0.0724746\n",
                        "plot           True         3.48441     99.6572\n"
                    ]
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "/Users/Edu/dev/ploomber/src/ploomber/cli/cloud.py:165: UserWarning: Issue: Non cloud users can monitor up to 2 pipelines simultaneously\n",
                        "  warnings.warn(content)\n",
                        "\r",
                        "  0%|          | 0/6 [00:00<?, ?it/s]\r",
                        "Building task 'filter_sales':   0%|          | 0/6 [00:00<?, ?it/s]\r",
                        "Building task 'filter_prices':   0%|          | 0/6 [00:00<?, ?it/s]\r",
                        "Building task 'group_sales':   0%|          | 0/6 [00:00<?, ?it/s]  \r",
                        "Building task 'join':   0%|          | 0/6 [00:00<?, ?it/s]       \r",
                        "Building task 'join_dump':   0%|          | 0/6 [00:00<?, ?it/s]\r",
                        "Building task 'plot':   0%|          | 0/6 [00:00<?, ?it/s]     \n",
                        "\r",
                        "Executing:   0%|          | 0/4 [00:00<?, ?cell/s]\u001b[A\n",
                        "\r",
                        "Executing:  25%|██▌       | 1/4 [00:02<00:07,  2.44s/cell]\u001b[A\n",
                        "\r",
                        "Executing: 100%|██████████| 4/4 [00:02<00:00,  1.87cell/s]\u001b[A\r",
                        "Executing: 100%|██████████| 4/4 [00:03<00:00,  1.32cell/s]\n",
                        "\r",
                        "Building task 'plot': 100%|██████████| 6/6 [00:03<00:00,  1.70it/s]\r",
                        "Building task 'plot': 100%|██████████| 6/6 [00:03<00:00,  1.70it/s]\n",
                        "/Users/Edu/dev/ploomber/src/ploomber/cli/cloud.py:165: UserWarning: Issue: Non cloud users can monitor up to 2 pipelines simultaneously\n",
                        "  warnings.warn(content)\n"
                    ]
                }
            ],
            "source": [
                "%%bash\n",
                "ploomber build"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "a9d075b6",
            "metadata": {
                "papermill": {
                    "duration": 0.032336,
                    "end_time": "2022-05-14T00:02:28.539407",
                    "exception": false,
                    "start_time": "2022-05-14T00:02:28.507071",
                    "status": "completed"
                },
                "tags": []
            },
            "source": [
                "The final output is a report: [output/plot.html](output/plot.html)."
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3 (ipykernel)",
            "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": "3.9.7"
        },
        "papermill": {
            "default_parameters": {},
            "duration": 21.535347,
            "end_time": "2022-05-14T00:02:28.782582",
            "environment_variables": {},
            "exception": null,
            "input_path": "templates/spec-api-sql/_build/readme_preprocessed.ipynb",
            "output_path": "templates/spec-api-sql/README.ipynb",
            "parameters": {},
            "start_time": "2022-05-14T00:02:07.247235",
            "version": "2.3.4"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
}