mirror of
https://github.com/grafana/grafana.git
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184 lines
6.3 KiB
TypeScript
184 lines
6.3 KiB
TypeScript
import { css, cx } from 'emotion';
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import React, { PureComponent } from 'react';
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import { MetadataInspectorProps } from '@grafana/data';
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import { GraphiteDatasource } from './datasource';
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import { GraphiteQuery, GraphiteOptions, MetricTankSeriesMeta } from './types';
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import { parseSchemaRetentions, getRollupNotice, getRuntimeConsolidationNotice } from './meta';
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import { stylesFactory } from '@grafana/ui';
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import { config } from 'app/core/config';
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import kbn from 'app/core/utils/kbn';
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export type Props = MetadataInspectorProps<GraphiteDatasource, GraphiteQuery, GraphiteOptions>;
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export interface State {
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index: number;
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}
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export class MetricTankMetaInspector extends PureComponent<Props, State> {
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renderMeta(meta: MetricTankSeriesMeta, key: string) {
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const styles = getStyles();
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const buckets = parseSchemaRetentions(meta['schema-retentions']);
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const rollupNotice = getRollupNotice([meta]);
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const runtimeNotice = getRuntimeConsolidationNotice([meta]);
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const normFunc = (meta['consolidator-normfetch'] ?? '').replace('Consolidator', '');
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let totalSeconds = 0;
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for (const bucket of buckets) {
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totalSeconds += kbn.interval_to_seconds(bucket.retention);
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}
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return (
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<div className={styles.metaItem} key={key}>
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<div className={styles.metaItemHeader}>
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Schema: {meta['schema-name']}
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<div className="small muted">Series count: {meta.count}</div>
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</div>
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<div className={styles.metaItemBody}>
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<div className={styles.step}>
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<div className={styles.stepHeading}>Step 1: Fetch</div>
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<div className={styles.stepDescription}>
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First data is fetched, either from raw data archive or a rollup archive
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</div>
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{rollupNotice && <p>{rollupNotice.text}</p>}
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{!rollupNotice && <p>No rollup archive was used</p>}
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<div>
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{buckets.map((bucket, index) => {
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const bucketLength = kbn.interval_to_seconds(bucket.retention);
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const lengthPercent = (bucketLength / totalSeconds) * 100;
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const isActive = index === meta['archive-read'];
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return (
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<div key={bucket.retention} className={styles.bucket}>
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<div className={styles.bucketInterval}>{bucket.interval}</div>
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<div
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className={cx(styles.bucketRetention, { [styles.bucketRetentionActive]: isActive })}
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style={{ flexGrow: lengthPercent }}
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/>
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<div style={{ flexGrow: 100 - lengthPercent }}>{bucket.retention}</div>
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</div>
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);
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})}
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</div>
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</div>
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<div className={styles.step}>
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<div className={styles.stepHeading}>Step 2: Normalization</div>
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<div className={styles.stepDescription}>
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Normalization happens when series with different intervals between points are combined.
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</div>
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{meta['aggnum-norm'] > 1 && <p>Normalization did occur using {normFunc}</p>}
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{meta['aggnum-norm'] === 1 && <p>No normalization was needed</p>}
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</div>
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<div className={styles.step}>
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<div className={styles.stepHeading}>Step 3: Runtime consolidation</div>
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<div className={styles.stepDescription}>
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If there are too many data points at this point Metrictank will consolidate them down to below max data
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points (set in queries tab).
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</div>
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{runtimeNotice && <p>{runtimeNotice.text}</p>}
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{!runtimeNotice && <p>No runtime consolidation</p>}
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</div>
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</div>
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</div>
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);
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}
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render() {
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const { data } = this.props;
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// away to dedupe them
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const seriesMetas: Record<string, MetricTankSeriesMeta> = {};
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for (const series of data) {
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if (series.meta && series.meta.custom) {
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for (const metaItem of series.meta.custom.seriesMetaList as MetricTankSeriesMeta[]) {
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// key is to dedupe as many series will have identitical meta
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const key = `${JSON.stringify(metaItem)}`;
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if (seriesMetas[key]) {
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seriesMetas[key].count += metaItem.count;
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} else {
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seriesMetas[key] = metaItem;
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}
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}
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}
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}
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if (Object.keys(seriesMetas).length === 0) {
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return <div>No response meta data</div>;
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}
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return (
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<div>
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<h2 className="page-heading">Metrictank Lineage</h2>
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{Object.keys(seriesMetas).map(key => this.renderMeta(seriesMetas[key], key))}
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</div>
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);
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}
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}
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const getStyles = stylesFactory(() => {
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const { theme } = config;
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const borderColor = theme.isDark ? theme.palette.gray25 : theme.palette.gray85;
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const background = theme.isDark ? theme.palette.dark1 : theme.palette.white;
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const headerBg = theme.isDark ? theme.palette.gray15 : theme.palette.gray85;
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return {
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metaItem: css`
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background: ${background};
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border: 1px solid ${borderColor};
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margin-bottom: ${theme.spacing.md};
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`,
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metaItemHeader: css`
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background: ${headerBg};
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padding: ${theme.spacing.xs} ${theme.spacing.md};
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font-size: ${theme.typography.size.md};
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display: flex;
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justify-content: space-between;
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`,
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metaItemBody: css`
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padding: ${theme.spacing.md};
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`,
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stepHeading: css`
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font-size: ${theme.typography.size.md};
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`,
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stepDescription: css`
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font-size: ${theme.typography.size.sm};
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color: ${theme.colors.textWeak};
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margin-bottom: ${theme.spacing.sm};
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`,
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step: css`
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margin-bottom: ${theme.spacing.lg};
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&:last-child {
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margin-bottom: 0;
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}
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`,
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bucket: css`
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display: flex;
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margin-bottom: ${theme.spacing.sm};
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border-radius: ${theme.border.radius.md};
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`,
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bucketInterval: css`
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flex-grow: 0;
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width: 60px;
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`,
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bucketRetention: css`
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background: linear-gradient(0deg, ${theme.palette.blue85}, ${theme.palette.blue95});
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text-align: center;
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color: ${theme.palette.white};
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margin-right: ${theme.spacing.md};
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border-radius: ${theme.border.radius.md};
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`,
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bucketRetentionActive: css`
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background: linear-gradient(0deg, ${theme.palette.greenBase}, ${theme.palette.greenShade});
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`,
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};
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});
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