1 /*****************************************************************************
2 * RRDtool 1.0.33 Copyright Tobias Oetiker, 1997, 1998, 1999
3 *****************************************************************************
4 * rrd_format.h RRD Database Format header
5 *****************************************************************************/
12 /*****************************************************************************
13 * put this in your /usr/lib/magic file (/etc/magic on HPUX)
15 * # rrd database format
16 * 0 string RRD\0 rrd file
17 * >5 string >\0 version '%s'
19 *****************************************************************************/
21 #define RRD_COOKIE "RRD"
22 #define RRD_VERSION "0002"
23 #define FLOAT_COOKIE 8.642135E130
26 #define DNAN ((double)fmod(0.0,0.0))
27 #define DINF ((double)log(0.0))
30 #define DNAN ((double)(0.0/0.0)) /* we use a DNAN to
31 * represent the UNKNOWN
33 #define DINF ((double)(1.0/0.0)) /* we use a DINF to
34 * represent a value at the upper or
35 * lower border of the graph ...
39 typedef union unival {
45 /****************************************************************************
46 * The RRD Database Structure
47 * ---------------------------
49 * In oder to properly describe the database structure lets define a few
52 * ds - Data Source (ds) providing input to the database. A Data Source (ds)
53 * can be a traffic counter, a temperature, the number of users logged
54 * into a system. The rrd database format can handle the input of
55 * several Data Sources (ds) in a singe database.
57 * dst - Data Source Type (dst). The Data Source Type (dst) defines the rules
58 * applied to Build Primary Data Points from the input provided by the
61 * pdp - Primary Data Point (pdp). After the database has accepted the
62 * input from the data sources (ds). It starts building Primary
63 * Data Points (pdp) from the data. Primary Data Points (pdp)
64 * are evenly spaced along the time axis (pdp_step). The values
65 * of the Primary Data Points are calculated from the values of
66 * the data source (ds) and the exact time these values were
67 * provided by the data source (ds).
69 * pdp_st - PDP Start (pdp_st). The moments (pdp_st) in time where
70 * these steps occur are defined by the moments where the
71 * number of seconds since 1970-jan-1 modulo pdp_step equals
74 * cf - Consolidation Function (cf). An arbitrary Consolidation Function (cf)
75 * (averaging, min, max) is applied to the primary data points (pdp) to
76 * calculate the consolidated data point.
78 * cdp - Consolidated Data Point (cdp) is the long term storage format for data
79 * in the rrd database. Consolidated Data Points represent one or
80 * several primary data points collected along the time axis. The
81 * Consolidated Data Points (cdp) are stored in Round Robin Archives
84 * rra - Round Robin Archive (rra). This is the place where the
85 * consolidated data points (cdp) get stored. The data is
86 * organized in rows (row) and columns (col). The Round Robin
87 * Archive got its name from the method data is stored in
88 * there. An RRD database can contain several Round Robin
89 * Archives. Each Round Robin Archive can have a different row
90 * spacing along the time axis (pdp_cnt) and a different
91 * consolidation function (cf) used to build its consolidated
94 * rra_st - RRA Start (rra_st). The moments (rra_st) in time where
95 * Consolidated Data Points (cdp) are added to an rra are
96 * defined by the moments where the number of seconds since
97 * 1970-jan-1 modulo pdp_cnt*pdp_step equals zero (rra_st).
99 * row - Row (row). A row represent all consolidated data points (cdp)
100 * in a round robin archive who are of the same age.
102 * col - Column (col). A column (col) represent all consolidated
103 * data points (cdp) in a round robin archive (rra) who
104 * originated from the same data source (ds).
108 /****************************************************************************
109 * POS 1: stat_head_t static header of the database
110 ****************************************************************************/
112 typedef struct stat_head_t {
114 /* Data Base Identification Section ***/
115 char cookie[4]; /* RRD */
116 char version[5]; /* version of the format */
117 double float_cookie; /* is it the correct double
118 * representation ? */
120 /* Data Base Structure Definition *****/
121 unsigned long ds_cnt; /* how many different ds provide
122 * input to the rrd */
123 unsigned long rra_cnt; /* how many rras will be maintained
125 unsigned long pdp_step; /* pdp interval in seconds */
127 unival par[10]; /* global parameters ... unused
132 /****************************************************************************
133 * POS 2: ds_def_t (* ds_cnt) Data Source definitions
134 ****************************************************************************/
136 enum dst_en { DST_COUNTER=0, /* data source types available */
141 enum ds_param_en { DS_mrhb_cnt=0, /* minimum required heartbeat. A
142 * data source must provide input at
143 * least every ds_mrhb seconds,
144 * otherwise it is regarded dead and
145 * will be set to UNKNOWN */
146 DS_min_val, /* the processed input of a ds must */
147 DS_max_val }; /* be between max_val and min_val
148 * both can be set to UNKNOWN if you
149 * do not care. Data outside the limits
152 /* The magic number here is one less than DS_NAM_SIZE */
153 #define DS_NAM_FMT "%19[a-zA-Z0-9_-]"
154 #define DS_NAM_SIZE 20
156 #define DST_FMT "%19[A-Z]"
159 typedef struct ds_def_t {
160 char ds_nam[DS_NAM_SIZE]; /* Name of the data source (null terminated)*/
161 char dst[DST_SIZE]; /* Type of data source (null terminated)*/
162 unival par[10]; /* index of this array see ds_param_en */
165 /****************************************************************************
166 * POS 3: rra_def_t ( * rra_cnt) one for each store to be maintained
167 ****************************************************************************/
168 enum cf_en { CF_AVERAGE=0, /* data consolidation functions */
173 /* An array of predictions using the seasonal
174 * Holt-Winters algorithm. Requires an RRA of type
175 * CF_SEASONAL for this data source. */
177 /* An array of seasonal effects. Requires an RRA of
178 * type CF_HWPREDICT for this data source. */
180 /* An array of deviation predictions based upon
181 * smoothed seasonal deviations. Requires an RRA of
182 * type CF_DEVSEASONAL for this data source. */
184 /* An array of smoothed seasonal deviations. Requires
185 * an RRA of type CF_HWPREDICT for this data source.
188 /* A binary array of failure indicators: 1 indicates
189 * that the number of violations in the prescribed
190 * window exceeded the prescribed threshold. */
192 #define MAX_RRA_PAR_EN 10
193 enum rra_par_en { RRA_cdp_xff_val=0, /* what part of the consolidated
194 * datapoint must be known, to produce a
195 * valid entry in the rra */
197 /* exponential smoothing parameter for the intercept in
198 * the Holt-Winters prediction algorithm. */
200 /* exponential smoothing parameter for the slope in
201 * the Holt-Winters prediction algorithm. */
202 RRA_dependent_rra_idx,
203 /* For CF_HWPREDICT: index of the RRA with the seasonal
204 * effects of the Holt-Winters algorithm (of type
206 * For CF_DEVPREDICT: index of the RRA with the seasonal
207 * deviation predictions (of type CF_DEVSEASONAL).
208 * For CF_SEASONAL: index of the RRA with the Holt-Winters
209 * intercept and slope coefficient (of type CF_HWPREDICT).
210 * For CF_DEVSEASONAL: index of the RRA with the
211 * Holt-Winters prediction (of type CF_HWPREDICT).
212 * For CF_FAILURES: index of the CF_DEVSEASONAL array.
214 RRA_seasonal_smooth_idx,
215 /* For CF_SEASONAL and CF_DEVSEASONAL:
216 * an integer between 0 and row_count - 1 which
217 * is index in the seasonal cycle for applying
218 * the period smoother. */
219 RRA_failure_threshold,
220 /* For CF_FAILURES, number of violations within the last
221 * window required to mark a failure. */
222 RRA_seasonal_gamma = RRA_hw_alpha,
223 /* exponential smoothing parameter for seasonal effects.
225 RRA_delta_pos = RRA_hw_alpha,
226 RRA_delta_neg = RRA_hw_beta,
227 /* confidence bound scaling parameters for the
228 * the FAILURES RRA. */
229 RRA_window_len = RRA_seasonal_smooth_idx};
230 /* For CF_FAILURES, the length of the window for measuring
233 #define CF_NAM_FMT "%19[A-Z]"
234 #define CF_NAM_SIZE 20
236 typedef struct rra_def_t {
237 char cf_nam[CF_NAM_SIZE];/* consolidation function (null term) */
238 unsigned long row_cnt; /* number of entries in the store */
239 unsigned long pdp_cnt; /* how many primary data points are
240 * required for a consolidated data
242 unival par[MAX_RRA_PAR_EN]; /* index see rra_param_en */
247 /****************************************************************************
248 ****************************************************************************
249 ****************************************************************************
250 * LIVE PART OF THE HEADER. THIS WILL BE WRITTEN ON EVERY UPDATE *
251 ****************************************************************************
252 ****************************************************************************
253 ****************************************************************************/
254 /****************************************************************************
256 ****************************************************************************/
258 typedef struct live_head_t {
259 time_t last_up; /* when was rrd last updated */
263 /****************************************************************************
264 * POS 5: pdp_prep_t (* ds_cnt) here we prepare the pdps
265 ****************************************************************************/
266 #define LAST_DS_LEN 30 /* DO NOT CHANGE THIS ... */
268 enum pdp_par_en { PDP_unkn_sec_cnt=0, /* how many seconds of the current
269 * pdp value is unknown data? */
271 PDP_val}; /* current value of the pdp.
272 this depends on dst */
274 typedef struct pdp_prep_t{
275 char last_ds[LAST_DS_LEN]; /* the last reading from the data
276 * source. this is stored in ASCII
277 * to cater for very large counters
278 * we might encounter in connection
280 unival scratch[10]; /* contents according to pdp_par_en */
283 /* data is passed from pdp to cdp when seconds since epoch modulo pdp_step == 0
284 obviously the updates do not occur at these times only. Especially does the
285 format allow for updates to occur at different times for each data source.
286 The rules which makes this work is as follows:
288 * DS updates may only occur at ever increasing points in time
289 * When any DS update arrives after a cdp update time, the *previous*
290 update cycle gets executed. All pdps are transfered to cdps and the
291 cdps feed the rras where necessary. Only then the new DS value
292 is loaded into the PDP. */
295 /****************************************************************************
296 * POS 6: cdp_prep_t (* rra_cnt * ds_cnt ) data prep area for cdp values
297 ****************************************************************************/
298 #define MAX_CDP_PAR_EN 10
299 #define MAX_CDP_FAILURES_IDX 8
300 /* max CDP scratch entries avail to record violations for a FAILURES RRA */
301 #define MAX_FAILURES_WINDOW_LEN 28
302 enum cdp_par_en { CDP_val=0,
303 /* the base_interval is always an
306 /* how many unknown pdp were
307 * integrated. This and the cdp_xff
308 * will decide if this is going to
309 * be a UNKNOWN or a valid value */
311 /* Current intercept coefficient for the Holt-Winters
312 * prediction algorithm. */
313 CDP_hw_last_intercept,
314 /* Last iteration intercept coefficient for the Holt-Winters
315 * prediction algorihtm. */
317 /* Current slope coefficient for the Holt-Winters
318 * prediction algorithm. */
320 /* Last iteration slope coeffient. */
322 /* Number of sequential Unknown (DNAN) values + 1 preceding
323 * the current prediction.
326 /* Last iteration count of Unknown (DNAN) values. */
328 /* optimization for bulk updates: the value of the first CDP
329 * value to be written in the bulk update. */
330 CDP_secondary_val = 9,
331 /* optimization for bulk updates: the value of subsequent
332 * CDP values to be written in the bulk update. */
333 CDP_hw_seasonal = CDP_hw_intercept,
334 /* Current seasonal coefficient for the Holt-Winters
335 * prediction algorithm. This is stored in CDP prep to avoid
336 * redundant seek operations. */
337 CDP_hw_last_seasonal = CDP_hw_last_intercept,
338 /* Last iteration seasonal coeffient. */
339 CDP_seasonal_deviation = CDP_hw_intercept,
340 CDP_last_seasonal_deviation = CDP_hw_last_intercept,
341 CDP_init_seasonal = CDP_null_count};
342 /* init_seasonal is a flag which when > 0, forces smoothing updates
343 * to occur when rra_ptr.cur_row == 0 */
345 typedef struct cdp_prep_t{
346 unival scratch[MAX_CDP_PAR_EN];
347 /* contents according to cdp_par_en *
348 * init state should be NAN */
352 /****************************************************************************
353 * POS 7: rra_ptr_t (* rra_cnt) pointers to the current row in each rra
354 ****************************************************************************/
356 typedef struct rra_ptr_t {
357 unsigned long cur_row; /* current row in the rra*/
361 /****************************************************************************
362 ****************************************************************************
363 * One single struct to hold all the others. For convenience.
364 ****************************************************************************
365 ****************************************************************************/
366 typedef struct rrd_t {
367 stat_head_t *stat_head; /* the static header */
368 ds_def_t *ds_def; /* list of data source definitions */
369 rra_def_t *rra_def; /* list of round robin archive def */
370 live_head_t *live_head;
371 pdp_prep_t *pdp_prep; /* pdp data prep area */
372 cdp_prep_t *cdp_prep; /* cdp prep area */
373 rra_ptr_t *rra_ptr; /* list of rra pointers */
374 rrd_value_t *rrd_value; /* list of rrd values */
377 /****************************************************************************
378 ****************************************************************************
379 * AFTER the header section we have the DATA STORAGE AREA it is made up from
380 * Consolidated Data Points organized in Round Robin Archives.
381 ****************************************************************************
382 ****************************************************************************
385 (0,0) .................... ( ds_cnt -1 , 0)
389 (0, row_cnt -1) ... (ds_cnt -1, row_cnt -1)
396 ****************************************************************************/